SARS-CoV-2, the agent of the COVID-19 pandemic, emerged in late 2019 in China, and rapidly spread throughout the world to reach all continents. As the virus expanded in its novel human host, viral lineages diversified through the accumulation of around two mutations a month on average. Different viral lineages have replaced each other since the start of the pandemic, with the most successful Alpha, Delta and Omicron variants of concern (VoCs) sequentially sweeping through the world to reach high global prevalence. Neither Alpha nor Delta was characterized by strong immune escape, with their success coming mainly from their higher transmissibility. Omicron is far more prone to immune evasion and spread primarily due to its increased ability to (re-)infect hosts with prior immunity. As host immunity reaches high levels globally through vaccination and prior infection, the epidemic is expected to transition from a pandemic regime to an endemic one where seasonality and waning host immunization are anticipated to become the primary forces shaping future SARS-CoV-2 lineage dynamics. In this review, we consider a body of evidence on the origins, host tropism, epidemiology, genomic and immunogenetic evolution of SARS-CoV-2 including an assessment of other coronaviruses infecting humans. Considering what is known so far, we conclude by delineating scenarios for the future dynamic of SARS-CoV-2, ranging from the good—circulation of a fifth endemic ‘common cold’ coronavirus of potentially low virulence, the bad—a situation roughly comparable with seasonal flu, and the ugly—extensive diversification into serotypes with long-term high-level endemicity. On New Year’s Eve of 2019, a cluster of cases of pneumonia was reported in Wuhan, China. The causative agent was identified as a novel coronavirus and the first genome sequence was made available to the scientific community by mid-January 2020 . The intPhylogenetic analyses point to a Most Recent Common Ancestor (MRCA) of all sequenced SARS-CoV-2 dating back to late 2019 , 7. WhenGenome sequencing can shed only limited light on the early timeline of the pandemic due to the small number of genomes available from that period. Athough from March 2020 onwards, the global spread and genetic evolution of SARS-CoV-2 can be exquisitely documented. Over the first half of 2020, the global population of SARS-CoV-2 remained largely unstructured geographically, with the entire viral genetic diversity distributed in most regions of the world , 13, witFrom the start of the pandemic, multiple lineages emerged while others went extinct, thereby keeping the genetic diversity of the SARS-CoV-2 population in circulation at a largely constant level during the first 2 years. Until the near-simultaneous characterization of four variants of concern (VoCs) towards the end of 2020 and the Omicron VoC in late 2021, this lineage-replacement dynamic remained mostly unnoticed with the exception of the emergence and global spread of the B.1 lineage carrying the D614G mutation that swept the world during the early stages of the pandemic , 17. TheWith the pandemic in its third year, it is becoming increasingly urgent to understand the long-term evolutionary dynamics of SARS-CoV-2. Here, we first overview key aspects of SARS-CoV-2 genetic and immunogenetic evolution and draw parallels with what is known for other coronaviruses infecting humans. We conclude the review by delineating possible scenarios for the future.Alpha-, Beta-, Gamma- and Deltacoronavirus . IntDirect competition between lineages can only occur when two lineages simultaneously infect the same host leading to only one lineage being successfully transmitted. Even when SARS-CoV-2 is at high prevalence, such mixed infections are expected to be rare. Thus, the dynamic of lineage replacement is likely primarily fuelled by the emergence of new strains that are more transmissible and/or more able to (re-)infect hosts immunized through vaccination or prior infection. Additionally, fitness decay of lineages in circulation through the accumulation of slightly deleterious mutations (Muller’s ratchet ) may alsThe mutation rate of SARS-CoV-2 is fairly unremarkable among RNA viruses  despite Host antiviral defence mechanisms inducing mutations at specific sites of the viral genome , 158 repThe accumulation of diversity in SARS-CoV-2 motivated the development of classification schemes to designate distinct lineages . Among tWhile most mutations are expected to have no functional impact , some siThere is indeed evidence for extensive epistasis being involved in the evolution of SARS-CoV-2, with the fitness of viral lineages likely dependent on combinations of co-adapted mutations and deletions , 167. Moin vivo [Recombination is also key for viral evolution as it allows for the combination, within a single genome, of mutations that arose independently in different genetic backgrounds. It is generally accepted that the emergence of all three human epidemic coronaviruses: SARS-CoV-1 , MERS-Coin vivo . The lowAny replicating entity will be present in the future at a higher frequency if it makes more copies of itself . VirusesGenetic determinants of virulence are difficult to assess not least because virulence is not a property of a pathogenic strain alone, but rather of an interaction with its host. Moreover, there are currently no large datasets available with the necessary information to associate individual viral mutations with disease severity and outcomes, while controlling for key host factors such as age, sex or health. It is sometimes assumed that as a pathogen adapts to its host, it becomes intrinsically less virulent , 208. HoThe morbidity and mortality resulting from SARS-CoV-2 infection have however reduced significantly since the start of the pandemic and will do so further as an increasing proportion of the human population is immunized through natural infection and vaccination. However, the evolution of the intrinsic virulence of SARS-CoV-2 is difficult to predict. The Alpha and Delta variants were both associated with increased virulence, with a higher proportion of people infected requiring hospitalization , 211–214A lower frequency of severe COVID-19 and fatalities from Omicron infection appears to be the result of both intrinsic differences in the virus pathogenicity and widespread cross-reactive immunity . This isA major focus of SARS-CoV-2 research has been to understand the extent to which immunity generated by infection or vaccination can cross-recognize and cross-protect against different lineages of SARS-CoV-2 and other members of the Coronaviridae family. This is particularly relevant now that the global population consists of a mixture of individuals with different histories of exposure to SARS-CoV-2 antigens .The majority of immunocompetent individuals infected with SARS-CoV-2 develop both T and B cell responses and these remain detectable in the circulation for at least 9–12 months , 227. PrWaning of effective immune protection over time can occur through a combination of changes in the circulating virus itself and subsequent loss of recognition by the immune response (escape), and/or by reduction in the number of immune effectors over time, such as antibody levels (reflective of plasma cells), memory B cells (able to generate new affinity-matured antibodies upon re-infection) and memory T cells. SARS-CoV-2 binding and neutralizing antibodies show measurable waning post-infection , 235, whin vitro by convalescent and post-vaccination serum relative to other strains [Many mechanisms can be used by viruses to evade adaptive immunity . For SARS-CoV-2, much attention has been paid to mutational changes in the spike protein . Point m strains , 241 andPrior to the emergence of the Omicron variant, the rise and decline of VoCs did not broadly correlate with immune escape. Alpha emerged with a comparatively small number of spike mutations but was relatively well neutralized by convalescent and post-vaccination serum . The BetAs with antibody recognition, mutations within epitopes can also lead to escape from the cellular arm of the adaptive immune response, whereby T cells fail to recognize infected cells, for instance due to the mutant epitope failing to be processed and presented effectively, or due to loss of recognition of the MHC-peptide complex by the T cells receptor (TCR) , 249–252in vitro at certain epitopes [Related to SARS-CoV-2 lineage cross-immunity, pre-exposure to non-SARS-CoV-2 viruses can mediate cross-immunity. For instance, T cell responses that can cross-recognize SARS-CoV-2 can be detected in pre-pandemic samples taken before it circulated in humans in as many as 80% of individuals, depending on the sensitivity of the assay used , 262–265epitopes  and thatepitopes .in vivo upon infection and vaccination [Prior immunity would be expected to limit disease on exposure to SARS-CoV-2 as pre-existing memory T cells are imbued with characteristics which make them more efficient at viral control than naïve T cells. As an example, pre-existing T cells that target the highly conserved core replication transcription complex  were shown to be enriched in health care workers that showed signs of exposure to SARS-CoV-2 but who appeared to control infection before it was detectable by PCR or induced an antibody response . This eacination  and havecination , 270 andcination , but epicination  or refutcination  that recCross-reactive antibodies are less common in pre-pandemic samples, likely due to their targeting of less conserved structural viral proteins and greater sensitivity to point mutations altering structural epitopes , 275 andThe biggest unknown in SARS-CoV-2 immunity is what correlates with protection from disease and from infection. Attempts have been made to identify antibody COP for SARS-CoV-2 by integrating efficacy and antibody data from vaccine trials , 279; hoCoronaviridae. Clinical trials are already underway to test second-generation vaccines to improve durability, infection and transmission blocking and the potential for inducing cross-reactive immunity that can protect against future SARS-CoV-2 variants and acquisition of novel coronaviruses in the future.A growing body of evidence suggests that vaccines for global health priorities should be focused on inducing both antibodies and T cells , 288. DuThe epidemic dynamic of respiratory viruses such as SARS-CoV-2 is driven by four main factors, namely, seasonality, viral evolution, population immunization rates and mitigation measures affecting host behaviour . SeasonatR), the average number of new infections caused by a single infected individual at time t. When tR is above 1.0, case numbers increase and when it falls below 1.0, they go down . However, during the endemic phase of COVID-19, morbidity and mortality will primarily be dictated by the ability of SARS-CoV-2 to bypass global host immunity and the intrinsic virulence of future SARS-CoV-2 variants.Gammacoronavirus infecting chicken, circulates in the form of multiple serotypes with highly divergent sequences in their spike protein and minimal cross-immunization between certain strains in circulation [Worst case scenarios include high case numbers and circulation of a diversity of serotypes with limited protection across strains, possibly fuelled by reverse anthroponosis and recombination between human and animal coronaviruses. For example, infectious bronchitis virus (IBV), a culation . This woculation . The extculation , 296. Hoculation .A situation where SARS-CoV-2 will circulate in the future as one or two serotypes at any time, constantly evolving through antigenic drift and with sequential lineage replacement represents the most likely scenario. This is the situation we currently experience with the four HCoVs and seasonal influenza A and B. People are generally infected for the first time to both HCoVs and seasonal influenza early in life and build immunity through multiple exposures throughout life. The majority of influenza infections are asymptomatic and people tend to get exposed to influenza multiple times ; the samCOVID-19 has been the worst respiratory disease pandemic since the ‘Spanish flu’ in 1918/20 when influenza H1N1 may have killed up to 50 million. The 1918–20 flu pandemic came to an end with H1N1 becoming the agent of endemic seasonal flu until it got displaced by the 1957–58 H2N2 pandemic lineage. The 1918 H1N1 lineage then made a comeback in the 1970s, again contributing to the yearly burden of seasonal flu until it got likely displaced in humans by the 2009 pandemic H1N1pdm09 lineage . While p


To date, human leukocyte antigens (HLA) have been the major focus in the approach to acute and chronic antibody-mediated rejection (AMBR) in solid-organ transplantation. However, evidence from the clinic and published studies has shown that non-HLA antibodies, particularly anti-endothelial cell antibodies (AECAs), are found either in the context of AMBR or synergistically in the presence of donor-specific anti-HLA antibodies (DSA). Numerous studies have explored the influence of AECAs on clinical outcomes, yet the determination of the exact clinical relevance of non-HLA antibodies in organ transplantation is not fully established. This is due to highly heterogeneous study designs including differences in testing methods and outcome measures. Efforts to develop reliable and sensitive diagnostic non-HLA antibody tests are continuously made. This is essential considering the technical difficulties of non-HLA antibody assays and the large variation in reported incidences of antibodies. In addition, it is important to take donor specificity into account in order to draw clinically relevant conclusions from non-HLA antibody assays. Here, we provide an overview of non-HLA solid-phase and cell-based crossmatch assays for use in solid-organ transplantation that are currently available, either in a research setting or commercially. In order to extend the outcomes of the transplanted allografts, a better understanding of the mechanisms of early and late ABMR and the development of protocols to combat and control these processes is needed –4. The id assays , 7, 23. d assays , 24–27. d assays –30. EffoIn this review, we surveyed the current literature and provided an overview of the recently published non-HLA antibody detection and cell-based crossmatch assays developed for use in solid-organ transplantation, either in a research setting or commercially. The articles reviewed in this report were selected based on two criteria: 1) whether the aim of the study was to test a new technology for the detection of non-HLA antibodies and 2) whether the study also included a correlation between the identified non-HLA antibodies and rejection episodes 5.in-vitro assays are considered to be relatively easy to perform, cost-efficient, and not labor intensive. In addition, it has been suggested that these assays could also detect antibodies against polymorphic antigens such as AT1R and MICA which might differ between donors, making their use in the clinical setting more justified.Cell-based crossmatching assays were among the very first techniques used to detect possible non-HLA antibodies in the serum samples from renal transplant patients , 49–51. In kidney transplantation, cell-based crossmatching assays have been used to screen for AECAs. In fact, the use of ECs as targets for crossmatches with fluorochromasia as the reporting technique has already existed since the late 1980s . An indiMing et al. made use of HUVECs for a retrospective crossmatching assay with flow cytometry . The ass1R-Ab and ETAR-Ab were measured using commercially available ELISAs  as target cells selected by the expression of angiopoietin receptor (Tie-2+). The assay was validated in a multicenter trial by Breimer et al. that used EPCs isolated from donor peripheral blood mononuclear cells (PBMCs) to screen for AECAs in the pre-transplant serum samples of 147 patients . The triSeveral other studies utilized the XM-ONE setup to screen for the presence of non-HLA antibodies in living donor kidney transplant recipients. These studies describe contrasting results , 27. Soy1R levels was found, indicating that the antibodies detected in the NHADIA results are not AT1R antibodies. The results from these studies suggest that non-HLA antibodies associated with the histology of ABMR which bind to CiGEnCs might recognize a wide diversity of antigens. In addition, these results point directly toward the major limitation of using a single cell line for endothelial cell crossmatching assays, as a single cell line does not address the variability of expressed antigens between individuals, which may form the basis for non-HLA antibody formation.The XM-ONE assay has several limitations. For instance, the properties of the circulating EPCs might not be reflective of the ECs in the transplanted allograft. It is essential to keep this in mind, especially since these precursor cells lack important general EC markers such as CD31 and CD34 . Additio1R is possibly not expressed by renal endothelial cells, and Delville et al. did not find a correlation between AT1R and non-HLA anti-endothelial cell antibodies, measured with the NHADIA  are complementary to the receptors in their native, non-denatured form  supplied by One Lambda, for detecting anti-ATpatients , 61, 62.1R or ETAR and the clinical outcomes in renal transplant recipients  in the post-transplant sera of kidney transplant recipients who had low or negative HLA-DSA. They observed an association between the development of ABMR and the levels of AT1R antibodies especially when HLA-DSA were detected   .Aside from ELISA, genome-wide analyses and protein microarrays have also been described for antigen identification –38. UsinIn protein microarrays, thousands of recombinant proteins are immobilized and arranged on a solid surface and are then probed with a fluorescent-labeled sample. Protein microarrays can be used for various applications, including antibody detection .® (a protein microarray technology), in combination with ELISAs, to identify target antigens for AECAs. The sera of 10 KTRs from a discovery cohort experiencing ABMR in the absence of HLA-DSA were used as samples for the protein array assay (in vitro) to be capable of endothelial cell activation and induction of pro-inflammatory cytokines and chemokines. To further validate these findings, an additional 150 pre- and post-transplant serum samples from KTRs were tested for antibodies against these antigens with in-house-made ELISAs, and positive results were observed in 24% of the tested pre-transplant samples. The presence of antibodies against these antigens was associated with HLA-DSA sensitization, ABMR, and early transplant glomerulopathy  and solid-phase assays  that can detect or screen for potentially alloreactive antibodies Figure 2y tissue , 60. ImpWhile several research groups in the field of kidney transplantation have utilized cell-based crossmatching assays to screen for non-HLA targets, this technique is rarely described in lung transplantation research.Margo and colleagues described an indirect immunofluorescence cell-based crossmatching assay to detect the presence of AECAs in the serum samples of lung transplant patients . CytocenIn addition to cell-based assays, antigen detection techniques such as ELISA  and SERE1R and ETAR in the pre- and post-transplant sera of 162 lung transplant recipients from three centers with a commercial sandwich ELISA kit  transfected with the genes of interest], which are then recognized by an IgG antibody  library which was then transfected to pulation . The respulation .1R and ETAR ELISAs), the exact contributions of non-HLA to graft dysfunction in lung transplantation is still unknown.In conclusion, non-HLA antibodies may contribute to the pathogenesis of lung transplant rejection, but their specific targets have yet to be identified Figure 21R and ETAR antibodies.Liver transplant recipients rarely experience ABMR even in the presence of antibodies directed against HLA . Althoug1R antibodies on the development of fibrosis in 42 pediatric liver transplant recipients using indirect fluorescence and a commercially available ELISA kit  were associated with a higher risk of rejection , the synergistic association between these antibodies increased the mortality risk significantly . Additionally, they reported that post-transplant non-HLA antibodies were capable of activating the complement system as seen from the positive C4d staining pattern in the liver tissue.In a larger cohort of adult liver transplant patients, O’Leary et al. reported that ATejection . They an1R and ETAR antibodies, the C-terminal laminin-like globular domain of perlecan (LG3) antibodies was also investigated in liver transplantation. Xu and colleagues recently published a study in which they tested the pre-transplant sera of 131 transplant recipients who received a second liver for 33 autoantibodies with a commercially available Luminex antibody panel (P = 0.02). Interestingly, patients with increased AT1R antibody levels in addition to LG3 were at a higher risk for rejection compared with those with either of these antibodies. A similar association was found between LG3 levels and HLA-DSA, which, once again, suggested a synergistic relationship. Therefore, screening for LG3 (in addition to AT1R and ETAR antibodies) might be important for liver transplant recipients with or without HLA-DSA as it may significantly help in identifying high-risk transplant patients.In addition to ATdy panel . Among t1R and ETAR antibodies in liver transplant patients, is gaining the attention of researchers. However, as it stands, research in this area is still relatively new and the reported data are limited  for antibody detection, and while these techniques are practical and allow for bulk sample processing with high throughput, they fail to consider donor and organ specificities Figure 21R and other non-HLA antibodies , and vascular endothelial growth factor A (VECGF-A) using commercial ELISAs. Additionally, the levels of pro-inflammatory cytokines IL-1, IL-6, and IFNγ were also investigated to evaluate the humoral response post-transplant. The authors reported that repeated episodes of rejection were associated with high levels of anti-AT1R and ETAR antibodies as well as increased levels of EC activation markers represented by higher titers of anti-VEGF-A and PAR-1 antibodies in one out of the six included patients. Interestingly, this patient did not develop anti-HLA antibodies. With regard to the pro-inflammatory markers, no elevations were observed for all the three tested cytokines . Although these findings are promising, it is difficult to highlight the importance of non-HLA in composite tissue transplantation making it difficult to draw firm conclusions at this stage, especially since both of the abovementioned studies base their conclusions on the observation from one single patient.Very recently, Sikorska et al. investigated the role of non-HLA antibodies in hand transplant rejection . The pos1R antibodies in the serum of transplant patients and the effect of several non-HLA antibodies on the allogenic endothelium .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The generation of tailored complex light fields with multi-core fiber (MCF) lensless microendoscopes is widely used in biomedicine. However, the computer-generated holograms (CGHs) used for such applications are typically generated by iterative algorithms, which demand high computation effort, limiting advanced applications like fiber-optic cell manipulation. The random and discrete distribution of the fiber cores in an MCF induces strong spatial aliasing to the CGHs, hence, an approach that can rapidly generate tailored CGHs for MCFs is highly demanded. We demonstrate a novel deep neural network—CoreNet, providing accurate tailored CGHs generation for MCFs at a near video rate. The CoreNet is trained by unsupervised learning and speeds up the computation time by two magnitudes with high fidelity light field generation compared to the previously reported CGH algorithms for MCFs. Real-time generated tailored CGHs are on-the-fly loaded to the phase-only spatial light modulator (SLM) for near video-rate complex light fields generation through the MCF microendoscope. This paves the avenue for real-time cell rotation and several further applications that require real-time high-fidelity light delivery in biomedicine. As an attractive tool for optical phased arrays, spatial light modulators (SLMs) facilitate holographic displays with high resolution. Hence, computer-controlled precise optical wavefront shaping is realized with SLMs, enabling new applications in microscopy8, holographic optogenetic stimulation9, optical manipulations10, and imaging through biological tissues13.Computer-controlled phased arrays shape the output beam of waves in a flexible way, having wide applications in astronomy16. Each MCF contains thousands of single-mode cores, and each fiber core can function both as an imaging pixel for detection of light17 and a light-emitting unit in a phased array for wavefront shaping18. When imaging through the MCF-based microendoscope, the discrete distribution of cores and the core-to-core spacing of the MCF limits the image resolution. Image reconstruction from speckle correlations is proved as an efficient method to further improve the imaging resolution20. The limited field of view was a trade-off for the needle size of lensless microendoscopes until recent advances on computational image recovery have demonstrated high-resolution widefield imaging through the microendoscope16. On the other hand, employing the MCF lensless microendoscope as a remote phased array can generate a diffraction-limited focus in the far-field of the distal application side of the microendoscope by displaying a Fresnel lens on the proximal facet18. This is implemented for two-photon endoscopic imaging, which is realized by scanning the focus using the phase-only SLM21. Similar approaches are also applied for multi-mode fiber (MMF) endoscopes with both amplitude and phase modulation, however, real-time generation of an arbitrary light field at the output is still challenging for MMFs 24.In clinical diagnostics, endoscopes are widely implemented for in vivo imaging with low invasiveness and offer diameters of a few millimeters. Recently, MCFs are employed for lensless microendoscopic imaging for minimum invasiveness of a few hundred microns25. The Gerchberg-Saxton (GS) algorithm26 is commonly utilized for calculating the CGH for structured light field generation. However, the random and discrete distribution of fiber cores leads to a strong spatial sampling of the CGH when coupling into the MCF, and the number of fiber cores is much less than the pixel number of the phase-only SLM, inducing strong aliasing. This results in unrecognizable reconstructed light fields27, proving the GS algorithm cannot be directly implemented in hologram generation for randomly distributed phased arrays. Therefore, we previously proposed a tailored phase retrieval algorithm for holographic control of complex light field through the MCF named Core-GS27, enabling complex wavefront shaping through the MCF with high fidelity. This is implemented in lab-on-a-chip optical manipulation of biological cells28 and can facilitate applications in holographic optogenetic stimulation29, micro-materials processing in hard-to-reach areas30, structured light generation for MCF amplifier31, and high-dimensional optical and quantum communication32. Due to the long computation time, the CGHs have to be generated in advance and then loaded to the SLM for dynamic light field generation. This limits applications such as adaptive tomographic optical manipulation33 and adaptive selective holographic photoactivation in optogenetics34, which needs rapid generation and refreshing of CGHs. Therefore, there is a great demand in biomedicine for real-time generation of tailored CGHs for MCF. Recently, deep neural networks have been used for reducing the computation time of CGHs39, however, all of these existing networks are designed for regularly distributed pixels and induce significant distortion when applied to discrete or randomly distributed phased-arrays27. Therefore, an approach that can generate tailored CGHs for MCFs or random phased arrays in real-time with high fidelity is not yet available.Implementing an adaptive lens and Galvo mirror for rapid 3D-scanning can break through the low refreshing rate of the SLM, enabling video-rate 3D fluorescent imaging through the microendoscopeIn this paper, we propose a novel phase encoder deep neural network (CoreNet), which can generate CGHs tailored for MCFs in real-time. The discrete or random distribution map of the phased array can be loaded to the neural network as an input. Hence, CoreNet can generate tailored phase modulation holograms rapidly, enabling precise light-field control for a randomly distributed phased array. Specifically, for holographic display through a lensless microendoscope with 10,000 single-mode fiber cores, the experimentally measured fiber core distribution map is embedded into the neural network to generate tailored CGHs. Unlike supervised learning, which needs to label the holograms calculated by classical phase retrieval algorithms as the ground truth, CoreNet uses an autoencoder network architecture to encode the target intensity into the tailored phase modulation holograms with unsupervised training. The diffraction model is incorporated into the network for numerically propagating the light field between the phase modulation plane and the target intensity plane. This allows the network to search for the optimal phase modulation maps of the target image and learn the mapping from the target images to the phase modulation holograms. This end-to-end architecture of CoreNet gets rid of complicated iterative operations, generating the phased array rapidly. Near video-rate generation of the tailored CGHs for MCF-based complex wavefront shaping can thus be realized by the trained network with high fidelity, opening new perspectives for applications based on MCFs.41 is employed. The phase differences between fiber cores are measured by off-axis digital holography28, and the conjugated phase differences are mapped on the MCF facet by the phase-only SLM to pre-compensate the OPDs. Then the MCF can act as a phased array to generate arbitrary light field distribution. Typically, to control the wavefront through the MCF, a tailored CGH generated by the Core-GS algorithm27 is loaded to the SLM additionally to the conjugated phase. Although the Core-GS algorithm can achieve good quality compared to the raw GS algorithm, the long iteration time still limits the applications.When light field transmits through the MCF with an extremely large number of fiber cores, the intrinsic optical path differences (OPDs) between fiber cores induce strong phase distortion in the light field coupled out. To compensate for this, digital optical phase conjugation (DOPC)We designed a phase encoder neural network, called CoreNet, to generate tailored holograms for high-speed complex wavefront shaping through MCF. The U-Net architecture has been proven to be effective in image processing tasks. Here, we modified the U-Net to a network with two inputs to collect more information on the target image Fig. . InsteadEach downsampling and upsampling block consists of two residual blocks as shown in Fig. 42 to simulate the light propagation. Compared with the classical angular spectrum method, it has less numerical error for far-field propagation. The band-limited angular spectral transfer function isAt the end of U-Net, the values at the core position are extracted to convolve with circular masks which indicate the shape of fiber cores to form the tailored phase modulation map for the phased array. Then the fiber core map as amplitude combined with phased array pattern to form a complex field. Finally, the complex field propagates to the target plane to form the target intensity distribution. Here we adopted band-limited angular spectrum method43. Several custom letters and binary patterns make up the test set. Sixteen images and two images are randomly selected from the MNIST validation dataset and custom test dataset to show the network performance. The reconstructed results by the Core-GS algorithm are also presented for comparison. CoreNet can achieve better reconstruction quality and the computation time is only 0.2 s, which is much faster than the Core-GS algorithm that took 11 s on the same platform  has been employed for selective photoactivation in a mouse brain with minor invasiveness34. However, the low SNR of the generated light field can lead to photoactivation of unwanted neurons, degrading the quality of the selective photoactivation. Holographic stimulation using CoreNet through the MCF can avoid this by generating tailored light fields with high fidelity, enabling in-vivo single-neuron activation. As shown in Fig. One potential application of CoreNet is in optogenetics. Holographic controlled light enables selective stimulation of target neurons individually with program-controlled shapes of the light field48. The small size and the high flexibility of optical fibers make them easy to be integrated into miniature lab-on-a-chip devices  as the loss function, which is defined in Eq. . The NPCNetwork training and testing were performed on a workstation with AMD Ryzen 9 3950X CPU and 128 GB of RAM, using NVIDIA RTX A6000 GPU. The network is trained for 5 epochs using the Adam optimizer. The training images are preprocessed to sizes of 512 × 512 pixels and then padded with zeros to 1920 × 1080 pixels.The experimental setup is illustrated in Fig. 49. In our work, we implement the previously proposed two-stage calibration method50. The intrinsic OPDs between the fiber cores are measured and compensated in transmission geometry, and the bending induced and temporal phase distortion is further calibrated by the back-reflected guide star in situ. To be more specific, a blazed grating is displayed on the SLM to generate a plane wave illumination at the proximal facet of the MCF, the distorted light field at the distal facet is imaged on the distal camera  through another microscope objective . A reference beam We demonstrated a novel phase retrieval method based on a deep neural network (CoreNet), decreasing the computational time of the CGHs for MCFs by a factor of 82. This enables real-time dynamic control of the complex light field through the MCF lensless microendoscope. The phase distortion in the MCF is compensated in situ by DOPC, which transforms the MCF to a holographic controlled phased array. Employing CoreNet to generate the tailored holograms for complex wavefront shaping through the MCF provides high fidelity holographic reconstruction at the distal side of the MCF. Near video-rate holographic display of dynamic light field is realized through an MCF with on-the-fly generated CGHs. Our work paves the path for high-speed MCF-based applications such as microendoscopic imaging, in vivo adaptive optical manipulation, optogenetic stimulation, micro-materials processing, and optical communication.Supplementary Legends.Supplementary Video 1.


A new family of phase change material based on antimony has recently been explored for applications in near-IR tunable photonics due to its wide bandgap, manifested as broadband transparency from visible to NIR wavelengths. Here, we characterize  When PICs are designed and optimised to satisfy one specific application, they are called application-specific photonic integrated circuits (ASPICs)6. Alternatively, programmable PICs7 are based on the idea that light in an integrated photonic circuit can be manipulated either by routing or coupling to satisfy more than one application in the same chip, reducing the specific performance but increasing the flexibility. With the growing need of emerging applications that require flexibility and re-configurability while providing low-cost and low-power consumption8, photonic field programmable gate arrays (PFPGA) are increasing in interest9. The intrinsic limitations of the SOI platform10 has lead to further development in compatible alternative platforms, capable of complementing the functionality of silicon, such as aluminium nitride (AlN), alumina,  polymers25 and BTO26 on SiNx, but require complex fabrication schemes such as planarization and bonding and can suffer from degradation at high operating temperatures resulting in potential yield issues29.The use of photonic integrated circuits (PICs) has gained momentum in recent years with increased functionality enabling multiple application domains, ranging from datacom32. The ability to reconfigure PICs has been implemented in applications ranging from memories33, wavelength division multiplexing34, and switches30 to neuromorphic devices37. Amongst all of the ternary phase change material in non-volatile integrated photonic systems, the most used is 39 the performance of GST-based integrated photonics is limited by strong coupling between the amplitude and phase modulation caused by a metal-insulator transition (MIT)41, which induces excessive optical loss in the crystalline phase of the material. This makes it ideal for reconfigurable silicon photonic applications such as switches, filters, memories and matrix multiplication, to name a few,43 by providing energy-efficient, compact, non-volatile operation.Phase change materials (PCMs) have been investigated in the context of PICs due to their non-volatile phase transition and for the high refractive index contrast between their amorphous and crystalline phases44 and optical field programmable gate arrays (OFPGAs)45. A new family of phase-change materials based on 47 has emerged as a promising candidate due to its low absorption in both states at k values lower than 1048, Mach-Zehnder interferometers (MZIs)49, Multi-Mode interferometers (MMIs)50 and ring resonator (RR) modulators on SOI52 have recently been demonstrated using these materials.A PCM with a wide band-gap, low absorption in both amorphous and crystalline states can ease these limitations and provide a path towards non-volatile phase modulation with low-loss for large scale neuromorphic computingIn this work, we experimentally demonstrate the tuning of RR building blocks on a silicon nitride hybrid platform based on 12. The patterns were defined by means of 245 nm deep-UV (DUV) lithography and then 300 nm fully etched ridge waveguides were made by an inductively coupled plasma reactive ion etching (ICP-RIE) process. A secondary lithography step was used to create windows for the PCMs deposition, after which a 20 ± 5 nm layer of 47. The PCM lift-off was completed by the sequential dipping of the samples in acetone and IPA at room temperature followed by NMP at 80 °C with ultrasonic agitation. Finally, a secondary 10 nm layer of 20−80% ZnS-Ring resonators were fabricated on 8” Si wafers with a 2 The spectral response of all the devices was characterised using a tunable laser source system (Agilent 8163B Lightwave Multimeter), followed by a C-band erbium doped laser amplifier (EAD-1K-C), an optical fiber attenuator (VOA50PM-APC), the device under test (DUT) and finally the InGaAs photo-detector. The polarization of the light was controlled to ensure that only TE modes could propagate through the devices and the measurements were normalized to extract the loss contribution of the input/output grating couplers and the waveguide. In order to extract the spectral response at different powers, the following methodology was used: Firstly, a 10 dBm pump-laser sweep was done before every single-wavelength laser exposure. Secondly, a 60 minute continuous exposure was done at a fixed wavelength within the resonant dip of the RRs. During the exposure, a photo-detector was used to monitor the response of the RR in real-time. Finally, a second low pump power (10 dBm) sweep was conducted for characterisation purposes, followed by a cool-down period of 5 minutes before the next high power fixed wavelength exposure cycle was performed on the same device. Two distinct pump-laser sweeps are done in order to monitor the position of the resonant wavelength of the RRs before and after each exposure.Different PCM lengths between 10 entclass1pt{minimaThe RRs exhibits a free-spectral range (FSR) of 1.92 nm at Q is produced when the PCM length is “zero” (bare RR) before and after the annealing process and we attribute this to the optical (refractive index) and electronic (sheet resistance) properties change induced by the annealing process  Fig. a and the(Q) Fig. b. In Fig51. We note similar ER and Q-factor trends for both the amorphous and the crystalline state of 42, where the RRs resonances in the crystalline state are cancelled due to the PCM losses55.The maximum ER value of 22.715 dB is achieved for a 56.In this section, we investigate the thermo-optical (TO) effect of k & t). An attenuator was used to adjust of the propagation loss in the waveguide and create a resonant mode in the RR cavity. The waveguide, bent waveguide and PCM section elements contains the simulated fundamental TE mode profile in the specific waveguide cross-section. Within the modelling of the dispersion and mode profile, ellipsometry values have been used for the SiNx, Through the commercial INTERCONNECT package of Lumerical Solutions, transmission models for the normalised devices are set up. The model consists of primary elements within the INTERCONNECT library and imported MODE profiles of cross-sections within the RR. Specifically, for the RR coupling section, the bidirectional directional coupler element was used, that takes as inputs the cross-coupling coefficients , Kun Gao et al.64 have demonstrated multi-level reversible and repeatable switching between the states of 57 (non-volatile tuning) of RRs integrated with a crystalline 49. The RR exhibits a FSR of 1.92 nm centred at In this section, we investigate the permanent trimmingn Sect. "", which 65. Figure 67. The high temperatures generated enhance diffusion rates promoting impurity doping such as sulfur70 and the reorganization of the crystal structure, resulting in a temperature-dependant change in the optical properties of 71.We speculate that the effect of crystal structure reorganisation and mobility of sulfur in To characterise the permanent trimming of the RRs, low-power  wavelength sweeps were done before and after laser exposure. When introducing an optical probe in the system, a portion of the optical power is absorbed and converted into heat. For a low-power probe, it is possible to read the transmission state of the 72 as follows:65. In this section, we demonstrated a passive permanent trimming method using only a continuous power optical probe without the interaction of optical pulse trains to assist in the phase transition exploiting the Sb2S3 in the crystalline state.To fully understand the non-volatile behaviour, high power exposures were undertaken using an erbium doped fiber amplifier (EDFA) coupled to a standard Fiber-DUT-Fiber optical transmission setup to obtain optical powers above −1.9 dBm inside the waveguide Fig. , as detaIn this paper, we demonstrate RRs building blocks exploiting an emerging family of low loss phase change materials in the C telecommunications band paving the way for future non-volatile electro-refractive modulation in mid index waveguides. RRs exhibiting a FSR of 1.92 nm, ERs > 20 dB and the ability to tune the whole FSR of a RR with a PCM cell length < 50


We theoretically analyze the waveguiding structures photo-induced by two incoherent counter-propagating Bessel beams (BBs) in a biased photorefractive crystal. We demonstrate that the cross-coupling of two BBs enables adressable channels and tunability of the forming guiding structures. The truncation parameter of the BBs, their Bessel orders and the misalignment between the two beams are all key parameters for tailoring the characteristics of the photo-induced waveguides such as the number of outputs, the output intensity levels and the distance between each output channel. Accordingly, we optimized the different parameters for designing not only a fully tunable Y-coupler but also optical splitters with up to five outputs and even more complex star couplers for all-optical interconnect applications. Finally, we report on the stability behavior of the photo-induced platform. The stability threshold depends on the nonlinearity parameter beyond which the beams display time-periodic, quasi-periodic and turbulent dynamics where spatially localized instabilities can be observed. All these results suggest more opportunities for fully controllable complex waveguiding structures and new all-optical solutions for active components in optical telecommunication and innovative ways of performing optical computing based on spatiotemporal chaos. Current optical devices, such as optical fibers, silicon waveguides, and photonic integrated circuits, are limited because those classical optical systems necessarily work with high peak power and are usually bulky, passive, and not reconfigurable3. Another widespread approach is based on scalable and reconfigurable optical techniques using Kerr or photorefractive (PR) medium. The latter is more appealing because of its low power requirements4 and its waveguide stability in 2D5. In a PR crystal, an incident light beam may photo-excite free charge carriers, which are then diffused and redistributed to finally create a space-charge field. This internal field modifies the refractive index distribution through the Pockels effect in the PR medium6. The light can therefore be confined and propagates inside the photo-induced refractive index structures, giving rise to all-optical waveguiding channels and switching devices7. In addition, thanks to the unique characteristic of PR crystals, it is easy to erase the photo-induced waveguides by launching an incoherent white light and address new configurations. Gaussian beams were the first and the most commonly used optical beams for photo inscriptions10. In recent years, unconventional beams, such as Airy beams, were also studied for photo-inducing complex waveguiding structures in all-optical routing applications due to their multi-lobes profiles and self-accelerating characteristic15.Optical interconnection technologies are expected to replace electronic systems for improving information processing performance due to their broad bandwidth, high speed, long-distance data transmission and potential reconfigurability16. Due to its mathematical definition, a BB possesses an infinite energy. Thanks to a truncation parameter (Gaussian term), BB can therefore be experimentally generated for example by using axicon or Spatial Light Modulators (SLM)17. Depending on its order, the BB intensity profile shows a central bright or dark spot and several surrounding rings18. Such symmetrical multi-lobes profiles and its non diffractive property make BB interesting for a variety of applications: free-space optical interconnects19, switches in the turbulent atmosphere20, light localization by inducing Bessel photonic lattices21.Similarly, Bessel beams (BBs), another unconventional beam proposed by Durnin in 1987, have also been extensively studied for their non-diffracting and self-healing properties22. Due to focusing phenomena, several integrated optical components, such as reconfigurable soliton networks23, multiport splitters24, and coupling strength tunable optical couplers25, have been created by multiplexing co-propagating diffraction-free BB in Kerr or PR crystal. Recently, we demonstrated that a single diffracting BB can also induce various waveguiding structures with multiple channels in a biased PR crystal, which can be tailored by different parameters such as truncation, nonlinearity, beam order, and beam size26. Despite this reconfigurability, the guiding properties of these photo-induced configurations are still limited by the chosen profile (order) of the BB. By contrast, the counter-propagating (CP) collision of two mutually incoherent beams gives rise to the cross-coupling of the beams’ lobes via the combination of the mixing refractive index structures generated inside the propagating medium. The cross-coupling enables more tunable parameters and more complex waveguiding configurations. In addition, the CP scheme also supports a dynamical instability mediated by intrinsic feedback27, which motivates the active optical components for all-optical routing and switching.BBs may also present self-trapping and breather-forming behavior when propagating in nonlinear media7, Airy beams14, and a single BB26, we show more complex optical couplers, addressing up to 9 outputs, with larger input-to-output shifts, wider distances between each output, and higher guiding efficiencies. By changing the parameters, such as the truncation of the beam, the transverse shift between the two CP BBs, and the order of each beam, we propose various reconfigurable waveguiding structures. We also demonstrate the flexible control for the number of the outputs, the guiding efficiency of each possible channel, and the distance between each output. In addition, we display and analyze the spatiotemporal dynamics of CP zero-order BBs and CP one-order BBs by varying the nonlinear strength. In both cases, the instability thresholds for the nonlinearity are identified beyond which the interactions lead to time-periodic, quasi-periodic, and turbulent dynamics where spatially localized instabilities can be observed.In this paper, we numerically analyze the waveguiding structures photo-induced by two incoherent CP BBs in a biased PR crystal. As already explained, the cross-coupling between two CP beams may generate more guiding channels and addressable outputs. By comparing with previous works based on Gaussian beams1). Both CP BBs propagate along the z-axis. The crystallographic c-axis of the crystal along which we apply the electric field corresponds to the x-axis. As discussed in28, the focusing effect in the c-axis direction, which is caused by the drift effect in the crystal, is much more intense. Consequently, to achieve waveguiding structures in a bulky 3D volume, the first step is to study the photo-induced configurations in the radial section composed of the c-axis and the direction of propagation (z-axis). For this reason, we restrict the BBs propagation in this plane, and the top view of the crystal in  displays our (1+1)D situation. The forward beam F and the backward beam B are injected respectively to the left  conc conc26, ons Eqs.  by Fast 26, the side lobes of a single diffracting BB focus and induce two efficient channels forming a Y-coupler during their propagation under the nonlinear condition. The head-on collision of such two incoherent beams results in the cross-coupling of the adjacent branch channels of the Y-couplers photo-induced by the forward and backward beam via the mixing refractive index structure, as shown in -direction. Figure D increases . When we vary this parameter, two CP BBs interact differently so that the two channels for the Y-coupler own different ratios of the guiding efficiency depending on their refractive index change distributions.According to the previous analysis, in a biased PR crystal, two mutually incoherent CP BBs may attract and overlap providing potential waveguiding structures with more accessible channels. As discussed in the case of Airy beamsentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimaD between two BBs can modulate the outputs number, the distance between the outputs, and the guiding efficiency of the channels. We should emphasize that using two CP BBs allows more flexible control of the guiding properties that is not possible to realize with one BB.In summary, as the unique parameter of CP schemes, the transverse distance All photo-induced waveguiding structures discussed above possess multiple potential outputs on each side of the crystal (at least nine), which allow them to meet the requirements for waveguides with multiple inputs-outputs in all-optical interconnects. Therefore, we consider the configuration with the most channels -direction. Figure 426. Besides, as shown in . Secondly, by changing the parameters, such as the Gaussian truncation (D), and the order of each incident BB (In conclusion, we numerically investigated the photo-inscription of waveguiding structures by two incoherent CP BBs in a biased PR crystal. Firstly, compared with the 1D waveguides induced by one single BB


Background: Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Deep learning methods have been widely used in the field of medical image analysis and have shown promising results in the diagnosis of CVDs. Methods: Experiments were performed on 12-lead electrocardiogram (ECG) databases collected by Chapman University and Shaoxing People’s Hospital. The ECG signal of each lead was converted into a scalogram image and an ECG grayscale image and used to fine-tune the pretrained ResNet-50 model of each lead. The ResNet-50 model was used as a base learner for the stacking ensemble method. Logistic regression, support vector machine, random forest, and XGBoost were used as a meta learner by combining the predictions of the base learner. The study introduced a method called multi-modal stacking ensemble, which involves training a meta learner through a stacking ensemble that combines predictions from two modalities: scalogram images and ECG grayscale images. Results: The multi-modal stacking ensemble with a combination of ResNet-50 and logistic regression achieved an AUC of 0.995, an accuracy of 93.97%, a sensitivity of 0.940, a precision of 0.937, and an F1-score of 0.936, which are higher than those of LSTM, BiLSTM, individual base learners, simple averaging ensemble, and single-modal stacking ensemble methods. Conclusion: The proposed multi-modal stacking ensemble approach showed effectiveness for diagnosing CVDs. Cardiovascular diseases (CVDs) are a global public health problem and result from a variety of causes. Since CVDs are a disease of multifactorial origin, it is not easy to accurately and timely diagnose the disease . Early aHowever, the ECG signal is complex and can be affected by various factors, such as noises and motion artifacts . This maConvolutional neural networks (CNN) are also widely used for diagnosing CVDs ,13,14,15Recently, there have been numerous studies conducted to detect CVDs using two-dimensional CNN (2D CNN) with ECG signals ,16,17,18However, a single CNN model may not be sufficient to accurately predict CVDs, as it may suffer from high bias or high variance . One wayThe major contributions of the study are outlined in the following manner: (1) for each lead, the performances of ResNet-50 based on scalogram images was compared to the performance of ResNet-50 based on ECG grayscale images; (2) we demonstrated that the diagnostic performance of the single-modal stacked ensemble was superior to that of the 12 individual base learners and single-modal simple averaging ensemble for both scalogram images and ECG grayscale images; (3) we proposed a multi-modal stacking ensemble that combines base learner predictions obtained from scalogram images and ECG grayscale images and then fed them as inputs to a meta learner; (4) the proposed multi-modal stacking ensemble demonstrated superior performance compared to LSTM, BiLSTM, 12 individual base learners, simple averaging ensemble, and the single-modal stacking ensemble.The dataset used in this research was a 12-lead ECG database that was collected by Chapman University and Shaoxing People’s Hospital in China . The 12-cwt.m function provided by Wavelet Toolbox in Matlab 2020a . The converted scalogram images were saved as 300 × 300 pixel RGB images. For ECG grayscale images, 1D ECG recordings were plotted as grayscale images with a white ECG signal against a black background. The ECG grayscale images were saved as 300 × 300 pixels. Examples of scalogram images and ECG grayscale images for the 7 groups  are shown in To utilize the 2D CNN model, it is necessary to transform the 1D ECG signal into a 2D image. Among the various methods of converting to a 2D image, we adopted a method of converting to a scalogram and a method of plotting a 1D ECG signal as it is in two dimensions. In this study, we refer to the former image as a scalogram image and the latter image as an ECG grayscale image. An ECG scalogram image is a visual representation of the time-frequency composition of the ECG signal that can reveal important information about the frequency characteristics of the ECG over time. Scalogram images were generated by applying the continuous wavelet transform (CWT) to the ECG recordings. An analytic Morse wavelet with a symmetry parameter of 3 (γ = 3) and a time-bandwidth product of 60  to implement logistic regression, SVM, and random forest classifiers, while XGBoost was implemented using XGBoost Python Package . Optimal hyperparameters for the meta learner were chosen by performing a thorough grid search and evaluating the accuracy of the validation set. The details of the hyperparameters which were tuned using the grid search are described in https://github.com/xodud5654/MMSE (accessed on 17 February 2023).ResNet is a deep neural network architecture introduced in 2015. It was developed to address the issue of vanishing gradients that arises in deep networks. This problem is resolved by adding skip connections between the layers. The skip connection is a type of feedforward network that involves a shortcut connection. It adds new inputs to the network and yields new outputs, enabling the network to learn the residual mapping instead of the original mapping. ResNet has achieved state-of-the-art accuracy in a variety of computer vision tasks and became one of the most popular architectures for image classification and computer vision tasks . For thiWe evaluated the individual base learner, simple averaging ensemble, and stacking ensemble methods on the publicly available Chapman University and Shaoxing People’s Hospital dataset. The data was split into three parts: 80% for training, 10% for validation, and 10% for testing. As represented in In For the single-modal ensemble methods, single-modal stacking ensemble methods achieved better results than the single-modal simple averaging ensemble and 12 individual base learners for both scalogram images and ECG grayscale images, as described in For the multi-modal stacking ensemble method, the best accuracy (93.97%), sensitivity (0.940), precision (0.937), and F1-score (0.936) were obtained when logistic regression was used as a meta learner as shown in In this study, we proposed a multi-modal stacking ensemble which combines information from different two modalities, scalogram images and ECG grayscale images. The ResNet-50 model was used as the individual base learner of the stacking ensemble, and one of the machine learning algorithms, logistic regression, SVM, random forest, and XGBoost was utilized as the meta learner. Logistic regression exhibited the highest accuracy, sensitivity, precision, and F1-score and XGBoost achieved the best AUC among the four machine learning algorithms when employed as a meta learner.The proposed multi-modal stacking ensemble relies on the predictions obtained from both the ECG grayscale image and the scalogram image to generate final predictions. The ECG grayscale image provides cardiologists with information similar to a patient’s ECG graph displayed on a monitor, while the scalogram image offers information about the time-frequency relationship of the ECG signals. In other words, the proposed model has the advantage of collecting multi-modal information potentially contained in the ECG grayscale image and the scalogram image, thereby enabling more accurate predictions of CVDs. From a practical perspective, the utilization of multi-modal information can be crucial for improving the accuracy of predictions in medical environments where accuracy is of utmost importance.There are many studies that have applied ensemble algorithms to the healthcare field. Kang et al. improved the AUC by simply averaging the predictions from five CNN algorithms  in classifying breast microcalcification in screening mammograms . Abdar eThere are various public ECG databases on the problem of arrhythmia classification: MIT-BIH arrhythmia database, CinC/Physionet Challenge 2017 database (CinC2017), China Physiological Signal Challenge 2018 database (CPSC2018), PTB-XL database, and Chapman University and Shaoxing People’s Hospital arrhythmia database ,35,36,37Despite demonstrating reasonable performance, this study has some limitations. First, with the exception of LSTM and BiLSTM, the majority of the experiments covered in the study are based on 2D CNNs. We compared the proposed method with base learners and single-modal ensemble methods to show the effectiveness of the proposed multi-modal stacking ensemble. However, it would also be worthwhile to compare the proposed method with feature-based machine learning algorithms or 1D CNN models. The second limitation pertains to the dataset utilized in this study. The 12-lead ECG arrhythmia database collected by Chapman University and Shaoxing People’s Hospital is based on severely imbalanced data. As described in In this study, we proposed the use of a multi-modal stacking ensemble for the prediction of CVDs. The proposed method achieved superior performance compared to LSTM, BiLSTM, individual base learner, simple averaging ensemble, and single-modal stacking ensemble methods. These results suggest that a multi-modal stacking ensemble may be a promising approach for improving the accuracy of CVD prediction. Further research is needed to explore the use of multi-modal stacking ensemble methods with large ECG datasets and other combinations of 2D CNNs and machine learning algorithms.


Background: The use of virus-neutralizing monoclonal antibodies has been approved in fragile populations, including kidney transplant recipients, who are at risk of developing severe COVID-19. Sotrovimab is the only currently available anti-SARS-CoV-2 neutralizing monoclonal antibody with activity against the new Omicron variant of concern. While sotrovimab has been approved in adolescents and adults, studies regarding its efficacy and safety in children aged less than 12 years old and weighing less than 40 kg are still lacking. Here, we report a first case of a child, who was treated early with sotrovimab after a kidney transplant. Case Report: At the end of January 2022, a 11-year-old male child underwent a deceased-donor kidney transplant and became infected with SARS-CoV-2 during the first day after surgery. Due to the increased risk of developing severe COVID-19, based on the predominance of Omicron and the patient’s renal function, the child was treated with sotrovimab. The clinical course was successful and no adverse reactions were reported. Conclusions: For the first time, we report the well-tolerated use of sotrovimab in children under 12 years old. As the pandemic affects children across the globe, urgent data on sotrovimab dosing in children with a higher risk of developing severe COVID-19 are needed. Kidney transplant recipients (KTRs) are particularly vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection due to the complex immunosuppressive regimens of transplantation and to the decreased T cell immunity ,2,3. TheAlthough infected pediatric patients have often shown a favorable COVID-19 outcome ,19, an iAt the end of January 2022, during the fourth pandemic wave, an 11-year-old male child of 36 kg, unvaccinated against COVID-19, with end-stage renal disease caused by nephronophthisis was admitted to the Department of Women’s and Children’s Health-University Hospital of Padova  and received a deceased-donor kidney transplant. Immunosuppression was induced with basiliximab and methylprednisolone according to the renal association’s clinical practice guidelines . The nasAlthough he was completely asymptomatic for COVID-19, 1 day after the kidney transplantation, the child tested positive for SARS-CoV-2 in the NPS performed for weekly periodical screening assessed in the PICU. A second NPS obtained 24 h later confirmed the infection, with an increasing viral cycle threshold. The child did not develop symptoms of infection, the laboratory data showed a modestly increased C-reactive protein level (13.62 mg/L), and the chest X-ray showed no pneumonia. As soon as the SARS-CoV-2 infection was confirmed, mycophenolate mofetil was discontinued, the methylprednisolone dose was progressively reduced according to the renal association’s clinical practice guidelines , tacroliHere, we report the management of SARS-CoV-2 infection in a pediatric KTR who became infected immediately after transplant and was treated with sotrovimab. This is the first described case of off-label use of sotrovimab in children under 12 years old to the best of our knowledge. SARS-CoV-2 infection after kidney transplant has a higher mortality rate than in the general population ,24; howeAlthough Bansal et al. have recently shown that pediatric solid organ transplant recipients develop mild COVID-19 , all SARTherefore, a gap in knowledge about the course of COVID-19 in the days immediately following the transplant, when immunosuppression is high, still remains.Discontinuation of early immunomodulant therapy combined with antiviral treatment with mAbs has been shown to have a beneficial effect on reducing severe outcomes in fragile adults affected by mild and moderate COVID-19 ,26,27. TConsequently, given the lack of knowledge on the risk of progression to severe COVID-19 a few days after the kidney transplant, off-label sotrovimab use was proposed, combined with immunosuppressant adjustments. The use of remdesivir in subjects under the age of 18 who do not require supplemental oxygen and who are at an increased risk of developing severe COVID-19 is not yet approved in Europe ; furtherConsidering our patient’s weight (36 kg) and the adult dose (500 mg), we obtained a dosage of 450 mg, similar to the recommended dosage in patients over 12 years. The difference between the calculated and standard dosages was only 10%. Taking into account, moreover, the clinical growth chart of our patient, which was substantially superimposable on that of an individual weighing 40 kg and aged 12 years, we decided to keep the dosage of 500 mg unchanged. Thus, we suggest this as a basic calculation of the dose of sotrovimab, taking this elementary revisitation of Clark’s formula into account. In addition to this, we recommend that clinical growth charts should always be considered.Sotrovimab combined with a temporary reduction in immunosuppression was well tolerated and associated with favorable clinical outcomes in our kidney transplant child diagnosed with mild COVID-19. To date, there are no published reports of children with COVID-19 treated with sotrovimab. Data on sotrovimab’s efficacy in adults are encouraging, and it appears to have an excellent risk–benefit profile. The most common adverse events reported in clinical registration studies conducted in adults are mostly related to hypersensitivity to the active substance and are mainly represented by nausea and a rash . The effThe growth of COVID-19 incidence in children has not yet been followed by an increase in the availability of antiviral drugs for the pediatric population, particularly in Europe, where the EMA did not extend the use of any antivirals  to subjects under 12 years old. Monoclonal antibodies are already approved in pediatric populations with pneumonia comorbidities for the prevention of respiratory tract infections, such as palivizumab, which is a specific monoclonal antibody used for the prevention of respiratory syncytial virus (RSV) infection in preterm infants . In lighThis case highlights a positive outcome for a KTR child with mild SARS-CoV-2 infection treated with sotrovimab. The patient tolerated sotrovimab without complications and with a favorable post-transplant outcome. As the pandemic affects children across the globe, urgent data on sotrovimab dosage in children with a higher risk of developing severe COVID-19 are needed.


Emerging evidences suggest that the trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS.We measured degeneration in limb-onset ALS patients  and controls (n = 12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. In order to investigate whether network spread on the brain connectome derived from healthy individuals were significant findings, we compared our findings against network spread simulated on random networks.The cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus  significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Network spread simulated on the random networks showed that the findings using healthy brain connectomes are significantly different from null models.Our findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS. Amyotrophic Lateral sclerosis (ALS) is a progressive and fatal neurogenerative disorder, which involves the degeneration of both upper and lower motor neurons . AlthougNeuropathological studies have shown the spreading of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) pathology in the central nervous system in ALS , 7. AlthThe spread of critical proteins in neurodegenerative diseases share similar pathological features. Besides ALS, the accumulation TDP-43 aggregates has been frequently observed in other neurodegenerative diseases including Frontotemporal Dementia (FTD) and Alzheimer’s disease  . In ALS,Computational models have been developed to characterise how the organisation of the human connectome may facilitate the spread and accumulation of abnormal proteins in the brain. Raj et al., 2012) first demonstrated that the microscopic consequences of the pathological spread throughout the human brain can be modelled using a computational network diffusion model (NDM), modelled in the structural brain connectome derived from the MRI of healthy individuals  first de, dementiTwo previous studies have used a similar computational approach to study network spread in ALS. Meier et al. (2020) showed that a computational random walker model can mimic progressive network degeneration in ALS . Their fIn this study, we investigated whether NDM can determine the severity (compared to controls) and progression (with-in group changes over 12-months) of neurodegeneration in limb-onset ALS. We implemented NDM as per previously published framework . We hypoEthics approval for this study was obtained from Calvary Health Care Bethlehem and Monash University Research Ethics Committees (Project Number: CF14/3968–2014002057). All the participants recruited in the study were over 18 years of age and gave informed consent.Individuals with ALS were recruited from the Neurology clinics at Calvary Healthcare Bethlehem and Barwon Health. MRI data were collected at three time points; baseline, six and twelve months. Fourteen participants (n = 14) with clinically possible, probable or definite limb-onset ALS and twelve (n = 12) age matched control subjects underwent MRI scans. Twelve patients with ALS participated in follow-up MRI scans that were acquired six months after the initial scans. Nine out of twelve patients, who participated in the six month follow-up scan, also participated in a twelve-month follow-up MRI scan . The revDisease duration was estimated based on patient report of symptom onset to time of baseline scan and follow-up scans. ALSFRS-R1, ALSFRS-R2, and ALSFRS-R3 are ALSFRS-R scores at baseline, 6 months, and 12 months follow-up respectively. ALSFRS-R: ALS Functional Rating Scale Revised. ALS: Amyotrophic Lateral Sclerosis. cervical: participants with cervical onset sub-type. lumbar: participants with lumbar onset sub-type. flial arm: participants with flial arm phenotype. ALSFRS-R scores range from 0 to 48, where a score of 0 represents severely impaired and a score of 48 is unimpaired.3, 192 slices per volume.MRI (T1-weighted) data for baseline and follow-up scans were acquired using a 3-Tesla Skyra MRI  at Monash Biomedical Imaging, Melbourne, Australia. The T1-weighted magnetization prepared rapid gradient echo (MPRAGE) images were acquired using the following parameters: acquisition time = 5 min 12 s, repetition time = 2300ms, echo time = 2.07ms, flip angle = 9°, field-of-view = 256mm, voxel size = 1 x 1 x 1 mmhttps://www.nitrc.org/projects/iit/). These connectome were generated using the previously developed HARDI template and probabilistic grey matter labels of IIT human brain atlas . The computational solution of λi and ui are the eigenvalues and eigenmodes of the of the Graph Laplacian H, and The computation of t = 0 to 50. The model times 0 to 50 was chosen as it was enough for diffusion to reach its peak and then establish an equilibrium. Network Diffusion was initiated from bilateral seeds, such that 41 different initial conditions were used. This process generated vectors with 80 elements at each time point (excluding seeds). The diffusivity constant alpha (α) was set to be 1. The NDM vector at all time points, f(t), were correlated against measured atrophy  using Pearson’s correlation, resulting in 41 × 51 correlation-time matrices. It is noted that the correlation was performed excluding the seed regions, in order to ensure that the correlation was not driven by the atrophy in the seed region [Based on the repeated seeding approach by Poudel et al. (2019) we implemented NDM  to identd region . The maxTo measure the atrophy in the parcellated brain regions, the mean and standard deviation (SD) of the volume were estimated. Regional volumes were normalized using total intracranial volume to correct for head size . For eacf(t)) using Pearson’s correlation, as described in the “Repeated seeding of network diffusion” section. Correlation values with p values (family-wise corrected based on brain regions) less than 0.05 were considered to be significant.The measured atrophy (t-scores) were correlated with the predicted atrophy , the right superior frontal gyrus  and the left lateral orbitofrontal gyrus . The other regions where we observed volume loss  are presented in the supplement .In ALS patients at six-months compared to baseline, the five most prominent brain regions showing volumetric loss were the left lateral occipital gyrus , right inferior temporal gyrus , left inferior temporal gyrus , right middle temporal gyrus  and left fusiform gyrus .In ALS patients at twelve-months compared to six-months, volume loss was observed in the brain regions including right bankssts  , left pars opercularis , left putamen , right precentral gyrus  and left caudal anterior cingulate . For visualization, the measured atrophy pattern (t-scores) for both cross-sectional and longitudinal analyses is displayed in The maximum correlation between the measured atrophy at baseline (ALS versus controls) and predicted atrophy was observed when the diffusion was initiated from two brain regions, namely the pars triangularis  and the pars orbitalis  . Fig 3, fusiform gyrus  and the lateral occipital gyrus  . Fig 4A When the measured atrophy was estimated between the ALS patients at six-month follow-up and at twelve-month follow-up, the maximum correlation between the measured and predicted atrophy was observed when the diffusion was initiated from brain regions including the caudal middle frontal , pars opercularis , superior frontal gyrus  and caudal anterior cingulate . The top ten seed regions identified by NDM are shown in Network spread simulated on the random networks showed that the findings using healthy brain connectomes are significantly different from null models. The distribution of maximum association between measured atrophy and predicted atrophy derived for the network spread on 1000 random networks are shown in the supplement . The disIn this study, we implemented a computational model of network diffusion to determine whether the severity and progression of neurodegeneration in limb-onset ALS can be modelled as a diffusion process. Our results can be summarized into three main findings. First, a volumetric analysis of the longitudinal MRI data from ALS patients revealed progressive neurodegeneration in extra-motor brain regions. Second, the NDM applied on a canonical structural brain connectome was able to recapitulate the cross-sectional and longitudinal pattern of spatial neurodegeneration in limb-onset ALS. Third, NDM revealed spatial shifts in the seeds of degeneration over time. These findings demonstrate the utility of connectome based model of network diffusion in predicting the evolution of neurodegeneration in limb-onset ALS.A cross-sectional comparison of the volumes within 82 brain regions between ALS patients at baseline and a group of healthy controls revealed degeneration in the right pars triangularis and superior frontal gyrus. Earlier structural MRI studies have consistently shown grey and white matter alterations in ALS –25. VoxeMore importantly, we observed a significant loss of brain volume within the ALS group within 6-month, which further progressed in 12-month. At 6-month, degeneration was observed in the left lateral occipital gyrus, and bilaterally in inferior temporal brain regions, which further progressed to the right bankssts , left pars opercularis, and left putamen at twelve-month follow-up. While only a limited number of studies have characterised ALS degeneration within a short time frame, most of these studies have shown a complex pattern of neurodegeneration involving multiple brain systems. These findings include progressive volumetric loss in the frontal grey matter , corticaTo understand the macroscopic processes driving the spatial pattern and severity of degeneration in ALS, we used a computational Network Diffusion Model as per the previously published framework , 13. We It is important to note that both cross-sectional and longitudinal analysis reveal the frontal and temporal brain regions as most likely seeds to be involved in trans-neural disease progression in ALS. Perhaps surprisingly, we did not observe the motor cortex as a region from which the disease spread was initiated. However, the average duration of disease in our cohort was approximately three years and we are observing early disease progression rather than disease onset. Given the insidious nature of disease onset and the time to diagnosis of one year on average , it is dPost-mortem findings suggest that pTDP-43 in ALS may disseminate in a sequential manner to disparate brain regions via axonal pathways, which underpins the identification of four neuropathological stages in the disease process . The iniAlthough TDP-43 pathology is observed in more than 90% of ALS population, there are discrepancies in findings related to the spread of TDP-43 inclusions in the central nervous system in ALS. Post-mortem findings suggest that TDP-43 aggregates spreads in the brain via neural projections , 36, wheThere are a number of limitations that need to be taken into consideration when interpreting the results. The sample size in our study is relatively small. Although we sought to minimize the heterogeneity using patients with limb-onset ALS, the cohort consisted of patients with different stages of disease severity. We were not able to recruit all of the patients scanned at baseline for follow-up scans, which further reduced the sample size for longitudinal analyses. The genetic factors and the patient’s medication history were not taken into consideration in the analysis. NDM is a first order linear model which does not take into account non-linear changes in the structural connectivity as the disease progresses. Furthermore, the NDM uses a healthy connectome from a healthy control group based on the assumption that structural network serves as a pathway for pathological spread, and remains unimpaired during the disease process . An inteIn summary, our findings suggest that the network diffusion model using the structural connectome may be a suitable tool for predicting severity and progression of degeneration in ALS. We showed that NDM can identify the brain networks that are responsible for pathological spread in ALS. In this study consisting of individuals with limb-onset ALS, extra-motor, frontal and temporal regions were more likely to be involved as the seed regions for network spread in ALS. Further studies with a focus on implementation of the NDM to investigate the degenerative pattern in individual patients may assist in addressing some of the issues identified in our study.S1 TableUncorrected p-values are reported as NDM modelling uses effect-sizes  to measure the strength of atrophy.(DOCX)Click here for additional data file.S1 FigHistogram of maximum correlation between predicted and measured atrophy  in 1000 random connectomes with preserved weight, degree and strength distributions of healthy connectome.(DOCX)Click here for additional data file.


This research aimed to investigate the tasks performed by Coronavirus Disease 2019(COVID-19) prevention and control management teams at primary healthcare (PHC) facilities during COVID-19 pandemic across the mainland China.An online survey was performed and COVID-19 prevention and control management teams at PHC facilities were invited to participate in this research. The top 7 most important tasks in the three different periods of COVID-19 containment were selected and ranked. Participations of tasks were surveyed.1 = 718) and Group B . The consensus was selected from top 7 most important tasks including screening at travel centers/intervals and screening at entry centers, at-home/centralized quarantine management, transferring, pre-examination/triage and fever sentinel surveillance clinic/fever clinic. Pre-examination/triage and fever sentinel surveillance clinic/fever clinic works became more significant in the regular prevention and control period. Adjusted analysis found that team members of Group A with a college, undergraduate college and graduate school educational background were less involved in pre-examination/triage works . Those who were over the median age were twice more likely to be engaged in managing fever sentinel surveillance of clinic/fever clinic visitors . Those being specialized in nursing and other specialties were less likely to participate in fever sentinel surveillance of clinic/fever clinic works . Those came from central and western China were less likely to participate in centralized quarantine management . Team members came from central and western China were twice less likely to participate in screening at travel centers/intervals .A total of 998 valid responses (an effective rate of 99.11%) were collected. The respondents were divided into Group A (≤5 respondents within each PHC facility, nIn mainland China, team members of COVID-19 prevention and control at PHC facilities are mainly responsible for screening, quarantine, transferring and monitoring during the COVID-19 pandemic. Pre-examination/triage and the fever sentinel surveillance clinic/fever clinic were gradually valued. Team members with lower educational background are competent in pre-examination/triage works, but more experienced general practitioners are more likely to be in charge of fever sentinel surveillance clinic/fever clinics work. The necessity of COVID-19 prevention and control management teams to participate in screening at travel centers/intervals is subjected to further discussions.The online version contains supplementary material available at 10.1186/s12875-022-01703-0. In the Coronavirus Disease 2019(COVID-19) pandemic, primary health care (PHC) systems, the first line of defense, faced unprecedented challenges . DespiteIn summary, there were two major aspects of healthcare work that primary care providers (PCPs) performed during COVID-19 pandemic globally. The first role PCPs played worldwide is to meet the citizens’ medical care needs as usual. Owing to the strong infectivity of SARS-CoV-2, some innovative diagnosis and treatment strategies were initiated. Telehealth and remote consultations were promoted to protect health care workers (HCWs) and reduce viral transmission by minimizing face-to-face visits . AlthougOn the other hand, as the “gatekeepers” of healthcare, PCPs also engaged in dealing with COVID-19 related high-risk populations as expected. COVID-19 patients’ identification and triage were also key elements for PHC workers . AccordiMoreover, except for management of high-risk populations, community containment measures such as quarantine, border closures and travel restrictions were also the main topic in the COVID-19 pandemic control process , 12. In As the COVID-19 pandemic situation changed over time, the curve of COVID-19 pandemic in China flattened since early April, 2020. There were some differences in the tasks of PCPs compared with other countries. This survey aimed to investigate the tasks conducted by team members of COVID-19 prevention and control at PHC facilities and members associated with these tasks during the COVID-19 pandemic across the mainland China.A cross-sectional survey was performed to investigate the tasks of primary care during the COVID-19 pandemic in mainland China, and the online questionnaire was distributed among PCPs from different PHC facilities across the country.Firstly, comprehensive literature search in English and Chinese electronic database were conducted to learn the community prevention and control tasks against COVID-19 pandemic domestically and globally. Subsequently, we interviewed 5 PCPs from Shanghai and Zunyi, Guizhou province and designed the original version of questionnaire. Then, we conducted face-to-face pilot interview from November to December, 2020. At 16 PHC facilities located in urban, urban-rural and rural areas of three selected cities , 32 members of COVID-19 prevention and control management team at PHC facilities were interviewed following the topic presented in original version of questionnaire. According to the advice of interviewees, we revised the questionnaire and eventually adapted it into an online version.The questionnaire was constructed and divided into 3 sections: 1) the basic demographics and professional specialties of the participants, including 9 questions; 2) selection and rank of the top 7 most important tasks from the total of 19 tasks for the three different periods of COVID-19 pandemic, referred to pre-outbreak period, outbreak period and regular prevention and control period. One supplementary answer was allowed or required if the task the respondents thought important was not included in the 19 tasks listed; 3) survey of the tasks participated by the respondents during the COVID-19 pandemic, including 9 questions ..The questionnaire was revised later according to the advice of interviewees, thus the final version should have a higher degree of validity than the original one. In addition, in the final version of the questionnaire, a supplementary answer was allowed by the respondents in case of any crucial items were missed in our questionnaire. However, the lack of new topic raised by the respondents further supported the content validity of the questionnaireP = 0.5, d and α was set at 0.05, thus the estimated minimum sample size was 385. To increase the validity of questionnaire, we enlarged the sample size by 20% and the final sample size reached 462.The sample size of PHC facilities was determined according to the calculation below. Based on the pilot field survey Following literature search, tele-interview and pilot field survey, we treated team members of COVID-19 prevention and control management at PHC facilities as target samples to meet program-specific requirements.Purposive stratified sampling was used in participant selection, and all of the 31 provinces & regions & Xinjiang Production and Construction Corps  in mainland China were included. Firstly, 5 city& districts were selected in each provinces & region, especially the city& district which was at high- or medium-risk during the COVID-19 pandemic. Secondly, in each city& district, 3 PHC institutions were chosen from its urban, urban-rural and rural areas. Sampling of PHC institutions was nonrandom and purposive. Next, team members of COVID-19 prevention and control management of each PHC facility were invited to complete the questionnaire. All of the participants were fully engaged in preventing and containing COVID-19 throughout the pandemic period. An online survey was carried out and the questionnaire was distributed by WeChat and website hyperlinks. The PHC facilities selected for the pilot interviews were avoided to participate in the online survey.An electronic consent form was provided before the beginning of the online survey. Once the informed consent was obtained, the questionnaire was valid. The president of reginal community health association from each provinces & regions was invited to ensure that the questionnaire distribution covered the PHC institutions from typical areas. For example, the PHC facilities from high- or medium-risk areas (the risk areas were defined by the number of confirmed cases during the last 14 days) were focused on in purposive stratified sampling. During the survey, two well-trained researchers were assigned to monitor the submission in case of over- or under-respondence in each region.The online survey was carried out from February 22, 2021 to March 2, 2021.chi-square test. A logistic regression model was employed to identify determinants of task participation. In the first step, associations between explanatory variables and task participation were analyzed. In the second step, all variables with p ≤ 0.25 in the first step were included in the adjusted analysis. The significance of crude odds ratio (OR) from univariate analyses and adjusted OR (aOR) in multivariate analyses were assessed. Size of a test was 0.05 (α = 0.05).All the data collected via online survey were extracted into an Excel spreadsheet using Microsoft Excel (Version 2019). Statistical analysis was performed using SPSS . The enumeration data were tested using To assist in selecting the top 7 tasks important in the pre-outbreak period, outbreak period and regular prevention and control period of COVID-19 pandemic, we calculated a composite index for each task selected by the respondents according to the following formula:Composite score = The formula took into consideration of the respondents’ personal prioritization on each task. The importance degree was ranked according to the weights 7 to 1, with the score of 7 being the most important task and 1 being the seventh important task. For example, the composite score of screening at travel centers/intervals in pre-outbreak period of COVID-19 pandemic was 6.02 .A total of 1007 participants from 421 PHC facilities submitted the questionnaire, of which 1001 agreed to participate in the survey and 998 completed the questionnaire (an effective rate of 99.11%).Preliminary data analysis showed that 280 respondents from 13 PHC facilities submitted questionnaires and the least number of respondents was 6 of each facility. However, in the pilot field survey, we learned at most 5 team members of COVID-19 prevention and control management were hired at each PHC institution. Then we compared the descriptive characteristics between the 280 and the other 718 respondents, and significant differences between the two groups were revealed   ; those being specialized in nursing and other specialties were less likely to participate in fever sentinel surveillance clinic/fever clinic works compared to those being specialized in general medicine   ; those came from central and western China were less likely to participate in centralized quarantine management compared to those came from eastern China    ; those who were from central and western China were more likely to participate in pre-examination/triage works compared to those who were from eastern China  ; those who were from western China were more likely to be involved in at-home quarantine management compared to those who were from eastern China, with the aOR: 2.86,  . Moreover, those who were from central China were less likely to be involved in screening at entry centers compared to those who were from eastern China, with the aOR: 10.37,   were similar between the two groups.Our survey found that in the pre-outbreak and outbreak period, screening at travel centers/intervals and entry centers were widely recognized as the most important factor among all the respondents. Unlike border closures and travel restrictions , 12, scrBesides, quarantine management both at-home and at centralized centers were also selected as the important tasks throughout the whole time of COVID-19 containment by both groups. In other countries, at-home quarantine might be managed by public health workers . HoweverAccording to our survey, it should be noted that pre-examination/triage and fever sentinel surveillance clinic/fever clinic work became increasingly important as the prevention and control of COVID-19 continues. During the transition from outbreak period to regular prevention and control period, confirmed COVID-19 patients from community transmission were declined and the focus of tasks changed to surveillance. Pre-examination/triage and fever sentinel surveillance clinic/fever clinic works within PHC facilities targeted in individuals who visited for any health problems or public care services. Team members here at PHC facilities routinely screened possible or probable COVID-19 cases through travel history asking, epidemiological risk evaluation and simple biochemical, imaging and physical examinations. The adjusted analysis found that team members with a higher educational background were less involved in pre-examination/triage work compared to those with a technical secondary school educational background. Because pre-examination/triage work mainly focused on travel history asking and temperature taking. However, fever sentinel surveillance clinic/fever clinics needs more experienced physicians to diagnose if there were possible or probable COVID-19 cases. Therefore, the results demonstrated that GPs above the median age were more likely to engage in fever sentinel surveillance clinic/fever clinic work. It should be mentioned that fever sentinel surveillance clinic was an innovative action in dealing with communicable diseases within PHC facilities in China.We also investigated COVID-19 patient treatment participation among COVID-19 prevention and control team members at PHC facilities. The adjusted analysis revealed that respondents of Group A from high-risk areas were more likely to participate in COVID-19 patient treatment. Risk areas in China was defined by the number of confirmed cases during the last 14 days. As the total number of confirmed cases in China was limited, the patient treatment was not the major task of PHC facilities. The results of consensus on the top 7 most important tasks reflected this actual situation. As for transferring of high-risk populations, another important task selected by team members of COVID-19 prevention and control was associated with the risk levels. Members of Group A from medium- or high-risk areas were more likely to be responsible for the transferring processes.Firstly, the participants were not randomly sampled; stratified purposive sampling was utilized to cover all of the regions of mainland China, the areas at different risk levels and in different intro-city locations. Secondly, this cross-sectional study surveyed the tasks of COVID-19 prevention and control team members involved in the mitigation of COVID-19 retrospectively, which might lead to information bias. The survey was carried out online, and only the questions in the questionnaire were addressed although supplementary items were allowed. In addition, after the final questionnaire was developed, a pilot survey should be conducted. During the survey, a supplementary answer was allowed by the respondents in case of any crucial items were missed in our questionnaire but no new topic was raised by the responses. The results supported the content validity of the questionnaire and overcame this shortcoming to some extent.In mainland China, team members of COVID-19 prevention and control at PHC facilities were mainly responsible for screening, quarantine, transferring and monitoring during the COVID-19 pandemic. The significance of the pre-examination/triage and fever sentinel surveillance clinic/fever clinic at PHC facilities increased from the outbreak period to the regular prevention and control period. Team members with lower educational background were competent in pre-examination/triage works but fever sentinel surveillance clinic/fever clinics work should be managed by more experienced general practitioners. The necessity of COVID-19 prevention and control management teams to participate in screening at travel centers/intervals remains to be further discussed.Additional file 1.