Papillary thyroid cancer (PTC)-desmoid type fibromatosis (DTF) is one of the rarest variants of PTC. The diagnosis is histological, and detecting a mutation of CTNNB1 in the mesenchymal component is highly suggestive of PTC-DTF. The treatment is essentially surgical. We conducted a review of all cases of PTC-DTF found in the English literature and our aim is to describe patient’s characteristics, histology, immunohistochemistry and somatic mutations of every case.BRAF-mutated) and another component of mesenchymal proliferation (CTNNB1-mutated). We conducted a literature review on PTC-DTF. In total, 31 articles were identified, that together reported on 54 patients. The mean age was 47 years, with a 2.2:1 female predominance. No ultrasound features were found to be helpful in differentiating PTC-DTF from other PTC variants. Of the 43 cases that reported histological details, 60% had locally infiltrative disease (T3b or T4). Around 48% had cervical lymph node metastases, but none had distant metastases. While PTC-DTF may be locally more aggressive than classic PTC, its overall behavior is similar and can include extrathyroidal extension and lymph node metastases, which may contain a stromal component and show extranodal invasion. The mainstay of treatment for PTC-DTF is surgery, and the DTF component is not expected to be sensitive to radioactive iodine. External radiotherapy, non-steroidal anti-inflammatory drugs, tyrosine kinase inhibitors and chemotherapy have also been used in selected cases. Due to the rarity of these tumors and the lack of specific treatment guidelines, management should be discussed in a multidisciplinary team.Desmoid-type fibromatosis (DTF) is a very rare variant of papillary thyroid carcinoma (PTC). It is essentially a dual tumor with a component of classical PTC with malignant epithelial proliferation ( BRAF mutation in the epithelial component and CTNNB1 mutation in the mesenchymal component), we proposed the term “papillary thyroid carcinoma with desmoid-type fibromatosis” (PTC-DTF) [Papillary thyroid carcinoma (PTC), follicular thyroid carcinoma, Hürthle cell carcinoma, and their histological variants, represent differentiated types of thyroid carcinoma (DTC), and account for the vast majority (>90%) of thyroid cancers . In 2009PTC-DTF) . In the A review of the literature was conducted in December 2020 in the Pubmed database, limited to publications in the English language, and using the following search strategy: thyroid carcinoma AND (desmoid-type fibromatosis OR nodular fasciitis-like stroma OR fibromatosis-like stroma). The literature search yielded 60 publications. We retained 26 relevant publications based on screening of the abstracts; 5 more were identified after screening the references from the literature search results. Thus, a total of 31 publications were included, comprising a total of 54 reported cases of PTC-DTF . The firUSP6 rearrangements, while fibromatosis CTNNB1 mutations [Although fibromatosis and nodular fasciitis share certain similar features such as cytologically bland spindle cells, they are two different entities, each with a distinct clinical course: nodular fasciitis is considered self-limiting and has the possibility to regress spontaneously, whereas fibromatosis tends to behave more aggressively . Morpholutations .CTNNB1 mutations [DTF is (myo)fibroblastic, locally aggressive and frequently recurring soft tissue neoplasm that develops in musculo-aponeurotic structures, favoring the extremities, the joints, the abdomen, the head and the neck ,36. Someutations  as demonutations ,38.There is no universally accepted terminology for these tumors. The DTF component was predominant in the most of reported cases (up to 95% in some cases), while others contained around 50% of PTC. Rebecchini et al.  proposedAs seen in About 48% of patients had lymph node metastases, which is towards the higher range of what has been reported for other DTC variants (20–50%), and is closer to aggressive variants of PTC . . For comAmong studies reporting tumors that were locally invasive (T3b/T4) or metastatic to lymph nodes (N1), several specified what component was responsible for the local invasion or lymph node metastasis, respectively. Among T3b/T4 tumors, in most cases, the DTF component was responsible for gross extrathyroidal extension; Wong et al.  reportedMost of the cases reported in the literature had a relatively short follow-up of around 1 to 2 years, and recurrences have been rarely described. The following cases illustrate that recurrences do occur and that long-term follow-up is warranted in patients with PTC-DTF. In a patient with locally invasive PTC-DTF  reported by Zhou et al. , initialTajiri et al.  describeThe diagnosis of PTC-DTF is challenging on fine-needle aspiration (FNA) samples. Takada et al.  reportedCTNNB1 mutation can also be present in anaplastic thyroid carcinoma [It is important to avoid misinterpreting the fibrotic component as benign reactive fibrosis. Moreover, the spindle cells may be mistaken for transformation into anaplastic thyroid carcinoma, and arcinoma . HoweverIn addition to the PTC-DTF cases listed in Immunohistochemical results of PTC-DTF are summarized in BRAF, most commonly c.1799T > A (p.V600E) [BRAF mutation can be found in about 45% of cases of PTC [CTNNB1 have been identified in the DTF component and currently include c.133T > C (p.S45P) [CTNNB1 have also been reported in some cases of the cribriform-morular variant of PTC [Mutational analyses have provided definitive evidence for the biphasic nature of PTC-DTF . The PTCp.V600E) , more rap.V600E) . Overalls of PTC . In contp.S45P) ,33, and (p.S45P) . Interes5P ,33, aDue to the rarity of PTC-DTF, no specific management guidelines exist in the literature. Management principles can be extrapolated from those applicable to other forms of DTC , as wellThe rate of distant metastasis in PTC variants ranges from 1% for the encapsulated follicular variant of PTC to 15% for the solid variant and the diffuse-sclerosing variant . InteresCTNNB1 in the DTF component can be helpful to establish the diagnosis of PTC-DTF.PTC-DTF is a very rare variant of PTC, with only 54 cases reported in the English literature. This variant may show a more aggressive behavior compared to classical PTC in terms of local infiltration and with a relatively high risk of lymph node metastases, but without any known cases of distant metastases to date. PTC-DTF is clinically and radiologically difficult to differentiate from other PTC variants. On FNA samples, a mesenchymal component is sometimes described, but it might not be considered relevant for the diagnosis, or its presence might lead to a misdiagnosis of schwannoma or sarcoma. Immunohistochemistry with positivity for nuclear β-catenin and the presence of somatic mutation of Clinicians should be aware of this variant, which needs to be included in the differential diagnosis of a rapidly growing thyroid nodule. For PTC-DTF, the first-line management is surgery aiming for a total resection; this is important because, in contrast to the classical PTC component that may respond to RAI, the DTF component should be considered RAI-refractory due to its mesenchymal nature. Patients with advanced or progressive PTC-DTF tumors should be managed by multidisciplinary teams with experience in the management of thyroid carcinomas and sarcomas including endocrinologists, high-volume surgeons, oncologists, radiologists and nuclear medicine specialists.


In 2021, three encephalitis cases due to the Borna disease virus 1 (BoDV-1) were diagnosed in the north and east of Germany. The patients were from the states of Thuringia, Saxony-Anhalt, and Lower Saxony. All were residents of known endemic areas for animal Borna disease but without prior diagnosed human cases. Except for one recently detected case in the state of Brandenburg, all >30 notified cases had occurred in, or were linked to, the southern state of Bavaria. Of the three detected cases described here, two infections were acute, while one infection was diagnosed retrospectively from archived brain autopsy tissue samples. One of the acute cases survived, but is permanently disabled. The cases were diagnosed by various techniques  following a validated testing scheme and adhering to a proposed case definition. Two cases were classified as confirmed BoDV-1 encephalitis, while one case was a probable infection with positive serology and typical brain magnetic resonance imaging, but without molecular confirmation. Of the three cases, one full virus genome sequence could be recovered. Our report highlights the need for awareness of a BoDV-1 etiology in cryptic encephalitis cases in all areas with known animal Borna disease endemicity in Europe, including virus-endemic regions in Austria, Liechtenstein, and Switzerland. BoDV-1 should be actively tested for in acute encephalitis cases with residence or rural exposure history in known Borna disease-endemic areas. Mammalian orthobornavirus 1) is one of the two known zoonotic members of the Bornaviridae family. For a long time, BoDV-1 has been noted to cause animal Borna disease (BD), a non-purulent meningomyelo-encephalitis of mainly horses and sheep in endemic regions of Germany, Liechtenstein, Switzerland and Austria [Crocidura leucodon) as a natural reservoir [The Borna disease virus 1  with a persistently BoDV-1 infected cell line for screening, followed by a line blot assay for confirmation . The linQuantitative reverse-transcription real time polymerase chain reaction (qRT-PCR) for BoDV-1  was condhttps://www.atgc-montpellier.fr/phyml/versions.php) with 1000 pseudo-replicates based on sequences from the nucleoprotein to the phosphoprotein gene . For the assessment of specific node support, subtree pruning and regrafting (SPR), branch-swapping and an approximate likelihood ratio test (aLRT) were performed. The Akaike information criterion was chosen as the model selection framework and the GTR + I + G as the best model.Molecular relationships of BoDV-1 sequences were analyzed by constructing a phylogenetic tree using the maximum likelihood method in PhyML 3.0  was performed according to the manufacturer’s instructions.Two acute cases (cases 1 and 2) with BoDV-1 encephalitis were detected, one of them fatal. In addition, one fatal case (case 3) was retrospectively diagnosed from a neuropathology archive.Case 1 was diagnosed in January 2021. The patient is a 58-year-old housewife from a rural region of Saxony-Anhalt  and no positivity in blood donors , possibl% and no intra vitam diagnosis of probable cases can be achieved by antibody detection in serum and CSF as illustrated here for case 1. The detection of bornavirus-reactive antibodies in serum is more sensitive than serology from CSF [Recently, a serological testing scheme with updated graded case definitions for human bornavirus encephalitis has been published , based ofrom CSF . In addifrom CSF . Howeverfrom CSF , as demofrom CSF ,7. The cfrom CSF , such asfrom CSF . Thus, dfrom CSF .Testing for BoDV-1 is often delayed due to lack of awareness, and the time to diagnosis may be several weeks while the patient’s disease progresses rapidly. In case 1 of our report, the time to diagnosis of three weeks was comparably short. In order to equip diagnostic laboratories with the skills and techniques to diagnose the infection, two external quality assurance tests have been successfully conducted for serologic and molecular testing ,20. BoDVBoDV-1 genomes are highly conserved, with a high genetic similarity between different strains and isolates of ≥95%. Nonetheless, sequencing the BoDV-1 genome and subsequent phylogeographic analysis might provide clues about the region of infection ,7,26. CoBrain histology of case 2 showed the typical features of BoDV-1 encephalitis  which isIn conclusion, these three newly diagnosed human BoDV-1 encephalitis cases outside of Bavaria confirm that bornavirus infection has to be routinely considered as differential diagnosis in human encephalitis cases in all regions endemic for BD in livestock and areas known to harbour BoDV-1 positive shrews. Besides parts of northern and eastern Germany, this includes also virus-endemic regions in Austria, Liechtenstein, and Switzerland. It is suspected that most case-patients become infected close to their often markedly rural places of residence, but an incubation period of weeks to months may mean they could present with acute illness outside of BD endemic areas as well. BoDV-1 should be actively tested for in all acute encephalitis cases with residence or rural exposure history in known BD-endemic areas of Europe.


COVID-19 caused by SARS-CoV-2 is responsible for the current pandemic. The US Food and Drug Administration recently granted emergency authorization of the Pfizer BioNTech and Moderna messenger RNA (mRNA) vaccines for use against COVID-19. Both vaccines utilize a novel technology, in which mRNA encoding the SARS-CoV-2 spike protein enveloped in lipid nanoparticles penetrates the cell membrane into the cytosol and produces a spike protein for subsequent antigen presentation and immune system activation.A 27-year-old Caucasian woman with a past medical history of anaphylactic reaction to tetracyclines presented for vaccination with the Moderna COVID-19 mRNA vaccine. Within a few hours of vaccination, subjective fever, fatigue, myalgias, and a mildly pruritic eruption under the breasts developed in the patient. Over the course of 3 days, the eruption spread to her abdomen, neck, and chest. The patient tried diphenhydramine and over-the-counter hydrocortisone 1% cream without relief. Physical examination showed variously sized erythematous and edematous papules and plaques on the abdomen, neck, and chest. Within the inframammary folds were numerous non–follicular-based pinpoint pustules . One wee,AGEP is a rare skin eruption characterized by an acute onset of numerous small, nonfollicular sterile pustules on an erythematous base. Clinically, AGEP is characterized by fever, with skin lesions typically beginning on the face or intertriginous zones  before disseminating over a few hours. Pruritus may also be present. Lesions normally last for 1 to 2 weeks, followed by superficial desquamation. More than 90% of cases of AGEP are due to the ingestion of drugs.Although AGEP is classically observed after drug ingestion, infection with SARS-CoV-2 may predispose certain patients to develop AGEP and AGEP-like pustular eruptions through its effect on the immune system. Notably, the inflammatory cytokine profile alterations and the cytokine storm in the setting of COVID-19 resemble the cytokine cascade changes in AGEP in many aspects.Currently, AGEP is a seemingly rare cutaneous adverse effect following the Pfizer BioNTech and Moderna COVID-19 mRNA vaccines. As more of the population receives the vaccine, it is likely that cutaneous adverse effects will develop in more individuals, with AGEP and AGEP-like pustular eruptions potentially being reported more frequently. It is important for clinicians to be aware of this entity in their COVID-19–vaccinated patients, as this will allow a more prompt diagnosis and appropriate evaluation to exclude more serious cutaneous reactions. Further studies will be needed to discover the significance of this dermatologic finding in the setting of COVID-19 and COVID-19 vaccination.None disclosed.


Serological tests based on the enzyme immunoassay (EIA) are the primary tool for the diagnosis of human immunodeficiency virus (HIV) in adults and have rapidly evolved to quicker, affordable and more accurate test formats to detect early HIV infection. Second- and third-generation HIV rapid tests detect the immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to the HIV and are used at the point of care and in HIV self-testing. The tests are affordable and accessible in state and private diagnostic laboratories. The present-day fourth- and fifth-generation EIAs can detect both p24 antigen and IgG and IgM HIV antibodies and thereby diagnose early HIV infection at approximately 2 weeks. The fourth- and fifth-generation EIAs also report sensitivity and specificity of more than 99%. The correct interpretation of HIV diagnosis of false-positive and false-negative EIA test results requires collaborative scrutiny of patient factors and laboratory test methodologies. Despite scientific advancement in HIV assay technology, clinicians are still challenged in a minority of patient cases by false-positive and false-negative HIV results.South Africa (SA) has one of the highest burdens of human immunodeficiency virus (HIV) infection in the world, and to achieve the 90-90-90 goals advocated by Joint United Nations Programme on HIV/AIDS (UNAIDS) accurate, early testing and linkage to care are required.4 Between days 11 and 13 post-infection, the protein (p24) antigen (part of the core protein of HIV) may be detected in newly infected individuals. Thereafter, an immune response is mounted. The appearance of immunoglobulin M (IgM) after approximately 2 weeks is followed closely by immunoglobulin G (IgG) at about 3 to 4 weeks post-infection. The viral RNA then decreases to a ‘set point’ level.6 This marks the end of early HIV infection. Detection of HIV during this early period is important because of the clinical benefit of instituting early antiretroviral therapy.8During the early phase of HIV infection after breach of the mucosal barrier, HIV infects target cells, for example, cluster of differentiation 4 (CD4) T lymphocytes. The primary viral amplification occurs in regional lymph nodes. This period is known as the eclipse phase (before the virus or antibodies are detected) and lasts approximately 10 days post-infection. Infected white blood cells and HIV virions then travel via the bloodstream to organs of the reticuloendothelial system and to lymphoid tissue in the gastrointestinal tract where secondary amplification occurs. This rapid replication and seeding of the virus results in very high ribonucleic acid (RNA) levels in blood, which becomes detectable from about 2 weeks post-infection.9 The median window period lasts approximately 18 days from infection and generally ranges between 10 and 24 days. Once antibody has appeared (seroconversion), antibody levels progressively increase over the next months and peak at 5–6 months. Thereafter, the antibody levels plateaux and remain fairly constant. Untreated, the infection evolves into advanced disease (Acquired Immunodeficiency Syndrome [AIDS]) characterised by opportunistic infections, a rise in HIV RNA levels and a gradual decline in HIV antibodies.10HIV is challenging to diagnose in the early ‘window period’ of infection. The window period is characterised by a lack of antibodies and p24 antigen.12 HIV antibodies (IgG and IgM) and the p24 biomarker are used in the diagnosis of HIV infection, and these biomarker profiles change from the point of infection . Enzyme-linked immunosorbent assays can detect HIV antibodies and/or p24 proteins. HIV ELISAs can be categorised into two formats: the indirect ELISA format is used in first- and second-generation HIV assays, and sandwich ELISA format is used in the third-fifth generation HIV assays . This resulted in HIV detection as early as 2 weeks post-infection.17 The HIV p24 antigen can also be used to detect HIV infection in late disease when the HIV antibody response wanes accompanied by HIV viraemia. In contrast to the fourth-generation assay, the fifth-generation assay quantifies HIV antibodies and p24 separately.18 HIV-1 and HIV-2 can be detected by the third-, fourth- and fifth-generation assays. In summary, the evolution of HIV assays has narrowed the window period for early HIV diagnosis.Enzyme immunoassay testing has undergone a progressive evolution over the last 25 years . First- 20 The sensitivity and specificity of the test thus provide an overall accuracy for the screening and diagnosis of HIV. The fourth-generation laboratory-based assays demonstrate sensitivity of ≥ 99.9% and specificity ≥ 99.5%, whilst the fifth-generation assay has shown 100% sensitivity and 99.5%, specificity.21 Minor variations in diagnostic performance of laboratory-based fourth- and fifth-generation assays occur between manufacturer kits.HIV testing is evaluated by the EIA diagnostic performance for HIV infection and non-infection. A screening test for HIV needs to be highly sensitive to detect all true-positive cases of HIV infection and therefore accurately reflect the number of positive HIV test results in patients infected by HIV (true positives). In contrast, the specificity of the test defines the number of negative test results in patients who have not been infected by HIV (true negatives). In all analytical testing systems, there is a trade-off between sensitivity and specificity of a test result.22In high prevalence settings, HIV diagnostic tests produce high positive predictive values for HIV infection. In SA, all screening tests undergo confirmation testing and subsequent work-up for patients suspected of HIV infection and are guided by the National HIV Testing Services policy.24 Errors can be caused at various steps in the laboratory testing process , for example, sampling errors, or during analysis of the patient specimen , for example, test kit variability or less commonly, after the specimen has been tested . Moreover, timing of testing and insufficient antibody titres may also cause false results.In clinical practice, one may encounter false-positive and/or false-negative HIV test results. Whilst these were more common with early generation tests, false results may still be reported using fourth and fifth generation assays. process .24,25,2627 This means that if the first test is reactive, it is followed by another RT from a different manufacturer to confirm the first screen reactive result. Alternatively, if the screening test yields a non-reactive result, it should be reported as negative. Discordant test results, that is, where the first result is reactive and the second result is non-reactive, should prompt a repeat RT following the serial national HIV testing algorithm. A laboratory HIV test is indicated in the case of persistently discordant results.Rapid tests (RTs) are a simplified version of an HIV ELISA and can detect p24 antigen and/or HIV antibodies. The South African National testing services policy, which reflects the World Health Organization (WHO) guidelines, recommend that adults are screened for HIV at the point of care using two RTs by a serial testing algorithm.30The advantage of HIV RTs is that it may be performed by non-laboratory staff and is usually completed in less than 30 min. Minimal equipment is required, and the test may be performed on finger-prick blood or oral fluid. This enables a result to be obtained at the time of consultation in various settings.32A disadvantage of RTs is decreased sensitivity compared to laboratory tests as blood from a finger-prick capillary sample has a lower concentration of antibodies and p24 antigen than a serum or plasma sample. Consequently, the detection of HIV is likely to lag by a few days in comparison to a laboratory test. Moreover, the HIV RTs are not as sensitive in comparison with higher test diagnostic accuracy of laboratory-based tests for early HIV infection.33 This can cause a false-negative diagnosis, delay initiation of antiretrovirals, increase HIV transmission and delay contact tracing.34 Of note, the fourth-generation RT does not perform superiorly to the third-generation rapid assay in detecting early HIV infection.35 Therefore, an improved fourth-generation RT, for key high incidence populations such as men who have sex with men (MSM) and those on pre-exposure prophylaxis (PrEP), may provide an advantage by identifying early HIV infection. In 2015, Alere released a re-formulated fourth-generation RT kit Alere™ HIV Combo. In the Vaginal and Oral Interventions to Control the Epidemic (VOICE) study, 28% of infections missed by current third-generation RTs would have been identified with the use of the Alere™ HIV Combo.36 Further field research examining the feasibility and utility of this RT as part of the testing algorithm in SA is warranted.In SA, the state sector utilises the third-generation RTs to screen for HIV.37 Samples that may be used include finger-prick blood samples and oral fluid. A self-test is intended to be used by an untrained person and should be easy to use with clear instructions. Some devices are based on second-generation HIV assays and therefore less likely to detect early HIV infection. The OraQuick HIVST, currently used in SA, is based on a third-generation test. The HIV Self-Testing Africa (STAR) Initiative is a 5-year project working with national health authorities in six participating Southern African countries to scale up HIVST. Partners include the Wits Reproductive Health and HIV Institute (Wits RHI) and Society for Family Health (SFH). Only HIVSTs with a WHO pre-qualification are procured in SA.38 A list of approved HIVSTs and those in the pipeline can be viewed at https://unitaid.org/assets/HIVST-landscape-report.pdf.39 Distribution channels comprise health clinics, workplace programmes and over-the-counter sales.HIV self-testing (HIVST) is a procedure whereby a person collects their own sample, performs the test and interprets the result on their own.40 Community-based follow-up provides additional post-screen counselling with referral to confirmatory testing services.Target populations include sex workers, MSM, transgender people, people who inject drugs and mobile populations. Those on PrEP and in HIV-related clinical trials should not use HIVSTs because of potential false non-reactive outcomes. A non-reactive self-screening result should prompt a retest after 6 weeks with linkage to HIV prevention services, whilst a positive result requires further testing and management by a trained healthcare provider. Linkage strategies after HIV self-screening include a telephone hotline for referral and information contained in a care card.The early diagnosis of HIV in adults has been substantially improved with advancements in EIA HIV-testing technology. Enzyme immunoassay, however, still suffer from false-positive and -negative results and require careful interpretation together with clinical and laboratory inputs to correctly inform the diagnosis of HIV infection.


No elective shoulder arthroplasties were performed at our practice between 18 March and 11 May 2020. Outcome measures included LOS, same-day discharge, discharge location, and 90-day emergency department (ED) visits, readmissions and reoperations. There were no significant differences in baseline preoperative patient characteristics. Shoulder arthroplasty performed post-COVID was associated with a shorter LOS  and a higher rate of same-day discharge . The rate of discharge to skilled nursing facilities was similarly low between the groups . There was a significant reduction in the rate of 90-day ED visits post-COVID , while there were no differences in 90-day reoperation  or readmission rates . The COVID-19 pandemic seems to have accelerated the shift towards shorter stays and more same-day discharge shoulder arthroplasties, while reducing unexpected acute health needs  without adversely affecting readmission and reoperation rates.The COVID-19 pandemic caused major disruptions to the healthcare system, but its impact on the transition to same-day discharge shoulder arthroplasty remains unexplored. This study assessed the effect of COVID-19 on length of stay (LOS), same-day discharge rates, and other markers of resource use after elective total shoulder arthroplasty. A total of 508 consecutive patients undergoing elective primary total shoulder arthroplasty between 2019 and 2021 were identified and divided into 2 cohorts: “pre-COVID” (March 2019–March 2020;  The coronavirus COVID-19) pandemic caused drastic disruptions to the provision of elective orthopedic surgery services in the United States 9 pandemi,3. The CElective shoulder arthroplasty is an increasingly popular and highly standardized procedure that has been classically performed as inpatient ,6,7. It This study sought to determine the impact of the COVID-19 pandemic on length of stay (LOS) and same-day discharge rates after elective total shoulder arthroplasty. Additionally, we examined discharge disposition patterns, ED visits, readmissions and reoperations. The hypothesis was that LOS decreased post-COVID, despite no change in patient characteristics.A retrospective study was conducted of a consecutive series of shoulder arthroplasties performed at a single private practice institution. Institutional review board approval was obtained for this study. Our registry was queried to identify all patients who underwent elective primary total shoulder arthroplasty (anatomic (ATSA) or reverse (RTSA)) between March 2019 and March 2021 by a single fellowship-trained shoulder surgeon. The inclusion criteria were: (1) an ATSA or RTSA and (2) minimum follow-up of 90 days. To achieve a homogenous sample of patients at low surgical risk, an a priori decision was made to exclude patients whose indication for surgery was traumatic, and those undergoing revision surgery.Following the 18 March 2020 recommendation by the Centers for Medicare and Medicaid Services to postpone non-essential surgeries in response to the COVID-19 virus, no elective shoulder arthroplasties were performed at our practice until 11 May 2020. As such, the study sample was divided into two cohorts: the “pre-COVID” group for surgeries performed before 18 March 2020, and the “post-COVID” group for cases performed on or after 11 May 2020. Notably, the treating surgeon had nearly 10 years of experience at the beginning of the study period. During the study period there was no change in postoperative protocols or in the design of the implants used by the primary surgeon.The main outcomes of interest included LOS (measured in hours after surgery) and same-day discharge. Discharge disposition (home versus skilled nursing facility (SNF)) was also recorded. Electronic medical records linked to the local hospital were reviewed to collect data on ED visits, readmissions, and reoperations within 90 days of surgery.Several patient characteristics that might affect the influence of the COVID-19 pandemic on resource allocation after shoulder arthroplasty were recorded. Specifically, data were collected on age, sex, body mass index (BMI), and the presence of co-morbidities including diabetes, chronic obstructive pulmonary disease (COPD), and tobacco use. Surgical location  data were also collected.p < 0.05 denoting statistical significance.To compare both baseline patient characteristics and postoperative outcomes between the pre- and post-COVID cohorts, Pearson chi-square tests were used for categorical variables and independent samples T-tests were used for continuous variables. Continuous variables were presented in terms of the mean and standard deviation (SD), and categorical variables were reported with frequencies and percentages. Statistical tests were 2-sided with A total of 508 patients met the study criteria. The study population consisted of 241 (47%) women and 267 men, with a mean (SD) age of 71 (8) years and BMI of 30 (6). Overall, 63% of patients underwent RTSA, while the remaining 37% had ATSA. There were no significant differences in any of the baseline patient characteristics between the pre- and post-COVID groups . There wp = 0.017) and higher rate of same-day discharge to home . There was a significant reduction in the rate of 90-day ED visits in the post-COVID cohort , while there was no difference with regard to 90-day reoperation  and readmission rates  to 7.4%. It may be that patients are now more likely to use and rely on technology  to address postoperative concerns that would traditionally warrant a visit to the ED. The observed reduction in ED visits may indicate that some of them are preventable with the use of technology and improved postoperative care coordination. This subject deserves further study. Although another explanation could be that patients were more fearful of postoperative ED visits, this is not supported by the lack of change in the 90-day complication or re-operation rate.The principal strengths of our study include its relatively large sample size and the fact that all procedures were performed by the same experienced surgeon, thus reducing surgeon variation in perioperative protocols. Nonetheless, our analysis was subject to several shortcomings that might be addressed in future research. First, the retrospective nature of this study does not allow causal inference. Therefore, we can only determine associations between COVID and the outcomes of interest. Second, because this study was performed at a private practice with a high pre-COVID rate of same day discharge shoulder arthroplasty, the results may lack generalizability. However, one might expect an even greater increase in the rate of outpatient shoulder arthroplasty among practices with traditionally higher rates of inpatient procedures. Future studies should evaluate and compare shoulder arthroplasty discharge patterns pre- and post-COVID across different practices and regions. Third, while we collected data on multiple markers of postoperative resource use , we did not assess patient experience and functional outcomes to better define the value equation. Fourth, there was a trend towards a potentially clinically relevant (+5.5% difference) higher rate of diabetes in the pre-COVID cohort compared to the post-COVID cohort which, while not yet significant, may affect results in larger samples. Finally, our study was limited in follow-up duration (90 days) due to the recency of the pandemic.This study provides evidence that the COVID-19 pandemic may have accelerated the shift towards shorter stays and more same-day discharge shoulder arthroplasties, while reducing unexpected acute health needs  without adversely affecting readmission and reoperation rates. These findings may be generalizable to other discretionary orthopedic procedures. Additional research should evaluate and compare the patient experience and functional outcomes following elective shoulder arthroplasty before and during the pandemic.


Aim: This study aimed to accurately identification of potential miRNAs for gastric cancer (GC) diagnosis at the early stages of the disease.Methods: We used GSE106817 data with 2,566 miRNAs to train the machine learning models. We used the Boruta machine learning variable selection approach to identify the strong miRNAs associated with GC in the training sample. We then validated the prediction models in the independent sample GSE113486 data. Finally, an ontological analysis was done on identified miRNAs to eliciting the relevant relationships.Results: Of those 2,874 patients in the training the model, there were 115 (4%) patients with GC. Boruta identified 30 miRNAs as potential biomarkers for GC diagnosis and hsa-miR-1343-3p was at the highest ranking. All of the machine learning algorithms showed that using hsa-miR-1343-3p as a biomarker, GC can be predicted with very high precision  using with the cut-off point of 8.2 for hsa-miR-1343-3p. Also, ontological analysis of 30 identified miRNAs approved their strong relationship with cancer associated genes and molecular events.Conclusion: The hsa-miR-1343-3p could be introduced as a valuable target for studies on the GC diagnosis using reliable biomarkers. Gastric cancer (GC) is a significant global health issue due to being the fifth leading cancer worldwide as well as the third cancer-related death leading cause, which leads to nearly 8,00,000 deaths annually . MorbidiRecent genome analysis revealed several biomarkers which are related to RNA, DNA, exosome, et cetera. A class of endogenous non-coding RNAs is MicroRNAs (miRNAs) (nearly 22 nt) which module the expression of the gene after transcription through degradation or translation blockage of target mRNAs . It is wMachine learning has several algorithms of which the decision trees (DT), random forests (RF), extreme gradient boosted trees (XGBT), and artificial neural networks (ANN) that have been frequently applied in medicine , particuArtificial neural networks have been broadly used in medical studies . Such moOversampling Technique (SMOTE). In the present study, for the first time, we aimed to use those new techniques for identification of GC related miRNAs with a reliable cut-of and highest possible accurecy in the external validation.Considering the important role of GC early diagnosis in patient’s survival rate and the lack of published article on identifying potential miRNAs for GC prediction at an early stage by AT, the present study aims to identify the potential miRNA for predicting GC by AT in the datasets of Gene Expression Omnibus (GEO) specifically with the stat of the art machine learning models. Traditional statistical models such as linear models previously has been used in looking for GC biomarkers and identified miRNAs with the potential prediction power , howeverhttps://www.ncbi.nlm.nih.gov/geo/. The dataset consist of the data of 2,566 miRNAs obtained from 2,759 non-cancer controls, and 115 GC cases (4%). In the original study the serum samples of cancer cases and non-cancer controls have been analyzed by microarray for miRNA expression profiles  for ontological analysis of the identified miRNAs list.GeneCodis is a web-based tool for the ontological analysis of lists of genes, proteins, and regulatory elements like miRNAs, transcription factors, and CpGs. It can be used to determine biological annotations or combinations of annotations that are significantly associated to a list of genes under study with respect to a reference list. As well as single annotations, this tool allows users to simultaneously evaluate annotations from different sources, for example GO Biological Process and KEGG. To this end, and before computing of genes . We usedOf those 2,874 patients included in this study, there were 115 (4%) patients with gastric cancer. This analysis consists of 2,566 miRNAs.Of those 2,566 miRNA in GSE106817 data, the Boruta algorithm initially selected 108 miRNA using Gini Index measurement (results are not shown here). The processing time was 17.24 minuts There were 77 tentative variables at the first stage. After fixing the tentative features, Boruta identified 156 miRNA for the analysis (results are not shown here). The process took 99 iterations convergence. It was observed that hsa-miR-1343-3p had the highest importance for prediction accurecy  among all identified miRNAs. The hsa-miR-1290 and hsa-miR-5100 had the second and third highest importance, with mean importance of 8.69 and 8.66, respectively .The balanced training data using SMOTE random oversampling technique had 1,376 cancer cases and 1,498 non-cancer controls. We trained DT, RF, LR, XGBT, and ANN perdition models with the selected miRNAs in the balanced training data.The external validation data GSE113486 had 40 (28.6%) gastric cancer and 100 (71.4%) non-cancer (controls). hsa-miR-1343-3 produced the highest prediction accuracy for GC prediction . For theAmong several models discussed in the study, the XGBT algorithm had better prediction accuracy overall (Table S1-S4). However, for hsa-miR-1343-3 all models had consistently 100% accuracy which indicates that this miRNA may strongly predict GC. For some miRNA such as hsa-miR-422a XGBT algorithm could predict GC with higher accuracy than the logistic regression and decision trees. Regulatory, functional, and perturbation analysis by GeneCodis 4 showed that 30 identified miRNAs  are relaal. . We also found three other miRNAs (hsa-miR-8073 and hsa-miR-1228-5p) with a great contribution to the GC prediction. A medical expert can use these findings for the early detection of GC instead of using costly and time-consuming tools such as colonoscopy Yao et al. .This study had several strengths compared to the previous studies. Compared to Shi et al. that identified the miR-1246 as the potential biomarker of GC that generated the AUC of 83%, our study identified the hsa-miR-1343-3p using the Boruta algorithm that led to a significant increase in the AUC . The stuAs depicted in The analysis of human phenotype and Mendelian inheritance ontologies identified Autosomal dominant inheritance and Global developmental delay among the most related phenomena with our miRNAs list. It is reported that gastric adenocarcinoma and proximal polyposis of the stomach is an autosomal dominant syndrome . Also, shttp://mirdb.org/). Among them transforming growth factor beta-1 (TGFβ1) play an important role in carcinogenesis upon binding its receptor (TGFBR1). It acts as a tumor suppressor by inhibiting cellular proliferation or by promoting cellular differentiation and apoptosis. However, it turns to be a tumor promoter by stimulating angiogenesis and cell motility, suppressing the immune response, and increasing progressive invasion and metastasis  family has been implicated in psychological and neurological diseases. However, few studies have explored the association between the expression of DLGAPs and different types of cancer. Liu et al. has suggested that the significant overexpression of DLGAP4 in GC may be a promising potential prognostic marker for GC . Aslo, LHowever, there were certain limitations in our study. We had relatively small sample size in GC group. Other limitations were the pathological information such as the tumor stage, age or other factors which were not available in our datasets. Nonetheless, the prediction accuracy of our model has high enough (100% AUC) for clinical use. Further, we were unable to do the survival analysis to further validate the markers identified in this paper based on public available data .Using several state of the art machine learning methods and Boruta algorithm, we identified several miRNAs that can predict GC. Specifically, hsa-miR-1343-3p, which identified by cut-off point of 8.2 may be nominated as a highly reliable biomarker for, GC diagnosis after meticulous empirical tests.


Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) images can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. During pandemic era, however, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensive and prone to error, which raises an urgent quest to develop practical autonomous solutions. In this context, first, the paper introduces an open-access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist. Second, a Deep Neural Network (DNN)-based framework is proposed, referred to as the  Numerous studies indicate that COVID-19 vaccination can effectively reduce disease transmission, hospital admissions, and deaths. However, due to new emerging COVID-19 variants4 the number of COVID-19 daily cases is still increasing in many areas. It is, therefore, crucial for healthcare professionals and authorities across the globe to find practical solutions to manage the COVID-19 pandemic and learn from this experience to be well prepared for potential future ones.Coronavirus disease 2019 (COVID-19) has been the world’s most threatening challenge of the twenty first century. According to the Coronavirus Resource Center of John Hopkins University (JHU)7. GGO refers to a slight increase in lung attenuation such that the underlying vessels are still observable8. The consolidation, on the other hand, is considered as a rise in lung intensity such that the underlying vessels are obscured8. The appearance of different types of imaging patterns and their location and distribution can be considered as specific signs of COVID-19 and provide helpful information for identifying the stage and severity of the disease12.Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold-standard test for the diagnosis of COVID-19. However, the RT-PCR test suffers from high false-negative rates and delayed results. Medical imaging, particularly Computed Tomography (CT) scans, has been recommended by the World Health Organization (WHO) as a complementary source of data for diagnosis and severity assessment of COVID-19. Countries with high rates of COVID-19 cases use chest CT images as the primary screening/monitoring technique. Several studies, therefore, have investigated COVID-19 manifestations on chest medical images. The most commonly observed chest imaging patterns in COVID-19 patients, as shown in Fig. 14, while the CT severity score is determined based on the spread of the COVID-19 lesions in each lobe15. During the pandemic era, when the number of patients is exponentially increasing, visual assessment and quantification of lung lesions by expert radiologists become expensive, laborious, and prone to error. Automatic segmentation of infectious regions can, therefore, help quantify the extent of lung involvement in patients confirmed with COVID-19, compute different severity scores, and speed up the treatment procedure.Evaluation and quantification of lung involvement in COVID-19 patients based on their chest images can help determine the disease stage, have an optimal allocation of the limited health resources, and make informed treatment decisions. Compared to other imaging modalities, CT imaging provides more accurate representations of COVID-19 lesions, making it the most informative imaging modality for the prognosis of COVID-19 pneumonia. Radiologists measure the COVID-19 lesions from the chest CT images and quantify the disease’s severity using different severity measures such as the Percentage of Opacity (PO) and CT severity score. The PO indicates the extent of involvement of the whole lung volumeContributions: Motivated by the urgent quest to develop accurate and reliable automated models for prognostic assessment of COVID-19 pneumonia, we introduce an open-access COVID-19 segmentation dataset along with a Deep Learning (DL)-based framework for the segmentation of COVID-19 lung abnormalities from chest CT images. In summary, the main contributions of this study are as follows:We propose the We produced a high-quality COVID-19 segmentation dataset containing 433 annotated chest CT images from 82 COVID-19 patients, annotated by a thoracic radiologist with 20 years of experience.We introduce an unsupervised enhancement approach that can mitigate the gap between the training set and test set and improve model generalization on CT images obtained by a different scanner, addressing a critical challenge in applying AI in medical imaging.A novel synthetic data generation (augmentation) pipeline is proposed that generates synthetic pairs of CT images and infection masks by inserting the infectious regions from COVID-19 CT images into healthy CT images. The proposed method improves the model performance by introducing more variability to the training set.Based on a set of comprehensive experiments, we have evaluated performance of the proposed In this section, we introduce the pixel-level labeled COVID-19 CT dataset along with the entclass1pt{minima16, which is accessible through Figshare16. A SIEMENS, SOMATOM Scope scanner has been used to obtain all axial CT images in 16.As stated previously, DL-based segmentation networks need a large number of annotated CT images for efficient training. Most of the private annotated datasets, which are the basis of the existing COVID-19 lesion segmentation models, are not publicly available. Open-source pixel-level annotated datasets can promote developing DL networks for COVID-19 lesions segmentation. Furthermore, evaluation of segmentation models on public datasets makes it feasible to fairly compare different segmentation models. To address the aforementioned issues, via this manuscript, we introduce a pixel-level annotated CT dataset, referred to as the 17. The trained model then takes the CT images as unseen test sets and predicts the infection masks. Next, a thoracic radiologist with 20 years of experience in lung imaging, carefully modified and verified the generated infection masks. Overall, we annotated 433 CT images from 82 COVID-19 patients. The patients’ average age is 18, probably because men are more vulnerable to the disease than women19. Besides, clinical findings indicate no association between CT scores and patients’ gender in COVID-19 pneumonia15.In the annotation process, a standard U-Net model is firstly trained on an open-access COVID-19 segmentation datasetCOVID-19 CT findings demonstrate that most COVID-19 patients have “bilateral” lung involvement, meaning that the disease affects both the right and left lungs. The COVID-CT-Rate dataset illustrates a similar behavior, i.e., 42% of CT images display bilateral lung involvement, 34% include lesions in the right lung, and the remaining 24% involve the left lung. The CT images are from diffident parts of the lung  with different infection rates. The distribution of lung area to the whole image area in COVID-CT-Rate is shown in Fig. The architecture of the proposed Encoding Path extracts informative features from chest images. The Encoding Path is initiated with a 22 to recalibrate channel-wise feature maps and improve model generalization. The input feature maps of each unit are then added to the output feature maps of SE module using element-wise addition operation. Transmitting the feature maps to the deeper levels  facilitates the convergence of the network and alleviates the gradient vanishing issue20.Context Perception Boosting (CPB) Module: As mentioned previously, a critical challenge in segmenting COVID-19 lung abnormalities is various sizes and scattered distribution of the regions of infection. Since a Convolutional Neural Networks (CNN)-based encoder extracts the information from a local area of the global image by applying the convolution kernel, it may fail to learn the long-range dependencies of the global image. To address this problem and inspired by21, we adopt a context perception boosting module in the last encoding block of the L2 regularization is applied after each convolution operation to penalize weight matrices and mitigate over-fitting. The outputs of different convolution layers are then integrated using element-wise addition. The dilation rates for parallel convolutions are set to 1, 2, 4 and 8. The context perception boosting module increases the receptive field of the extracted feature maps by incorporating multi-scale kernels, helping the network detect COVID-19 abnormalities of various sizes.Transition layer: A critical feature of the U-Net, that has also been leveraged in many other segmentation networks, is using skip connections to transfer encoding feature maps with higher spatial information to the decoding blocks. Here, we apply a transition layer, a Decoding Path takes the extracted features of the context perception boosting module as input, aiming to restore the spatial representation and generate masks indicating the regions of infection. For reconstructing precise infection masks, four decoding blocks are adopted within the decoder path of the 22 to calibrate channel-wise features. The output of the last decoding block has the same spatial dimension as the original CT images. A i, and N denotes the number of pixels for which Different types of scanners and image acquisition settings produce CT images with heterogeneous characteristics, resulting in poor performance of a trained deep learning-based model on unseen CT images. To address this critical challenge, we propose an unsupervised enhancement method for segmentation networks, illustrated in Fig. 24 has become a standard solution in different computer vision tasks to tackle the lack of sufficient training data and prevent over-fitting. These techniques mainly include applying simple transformations such as rotating, horizontal/vertical flip, zooming, and/or translation on original images. However, these transformations make minor changes to the existing images, resulting in limited diversity on newly generated images. Development of new data augmentation techniques is therefore crucial to introduce more variability to the synthetic samples for better data reinforcement. For example, recently, Ref.25 implemented a specific data augmentation approach into a generative adversarial segmentation network and achieved improved results. Capitalizing on this vision, we propose a specific approach for generating synthetic pairs of CT images and their corresponding infection masks by extracting the COVID-19 regions of infection from infected chest CT images and inserting them into healthy chest CT images. We use a chest CT image containing COVID-19 regions of infection alongside its infection mask and a chest CT image from a healthy person acquired by the same scanner alongside its lung mask as the input. First, CT images are normalized using the mean and standard deviation of their pixel intensities. Next, regions of infection are extracted from the COVID-19-infected CT image using the infection mask. Lung regions from the normalized healthy CT image are extracted using its corresponding lung mask. Then, the location of the region of infection is evacuated by element-wise multiplication of normalized healthy lung regions and the inverted infection mask. In the next step, the regions of infection are added to the evacuated healthy CT image. Finally, the regions of infection outside the healthy lung regions are trimmed off using the healthy lung mask, and the new infected CT image is generated. The synthetic infection mask is adjusted for the synthetic infected CT image by multiplying the healthy lung mask by the infection mask.Using data augmentation techniques26 and Focal Tversky Loss (FTL)27 and is given by28, is a loss function that improves the model performance by assigning higher weights to hard pixels and has been introduced explicitly for the segmentation of class-imbalanced datasets. The TI and FTL are defined as followsi belongs to the lesion class and i is of the background class. The 27. Compared to the standard For a segmentation task, when dealing with highly imbalanced datasets, which is common when dealing with COVID-19 chest CT images, equally penalizing False Negatives (FNs) and False Positives (FPs) during the training process will result in a low sensitivity in predicted masks. To tackle this problem, we trained the This research work is performed based on the policy certification number 30013394 of Ethical acceptability for secondary use of medical data approved by Concordia University, Montreal, Canada. All experiments were conducted in accordance with the Tri-Councel Policy Statement of Ethical Conduct for Research Involving Humans in Canada. Furthermore, informed consent is obtained from all the patients. To evaluate the efficacy and potential limitations of the proposed Dataset A17, which is a public dataset containing chest CT volumes of 10 COVID-19 patients, a total of 2, 581 CT images. The matrix size of the images is Dataset B (the  dataset): The in-house dataset, which contains 433 annotated CT images from 82 COVID-19 patients.Dataset C29, which contains nine COVID-19 chest CT volumes, a total of 829 CT slices. In total, 373 out of 829 CT images indicated COVID-19 lesions and have been segmented by a radiologist. The segmentation of lung regions has been performed for the whole CT volumes. The CT slices have a dimension of Dataset D, introduced in our previous work16, containing chest CT volumes from 76 healthy cases, 171 COVID-19 patients, and 60 patients with other types of Community Acquire Pneumonia (CAP). In this dataset, 55 out of 171 COVID-19 CT volumes have slice-level labels, indicating which CT slices demonstrate COVID-19 lesions. The CT images used for the The first two datasets (Dataset A and B) are used for training/testing purposes, while the third and fourth datasets (Dataset C and D) are used to evaluate the model’s performance from various aspects.Four datasets are used in different steps of the experiments as outlined below:We evaluate the Dice Similarity Coefficient (DSC), which is the most commonly used metric for segmentation. The DCS measures the relative overlap of the predicted regions of infection and their ground truth labels. It takes the value one as its maximum when the two regions have a complete agreement and a minimum value of zero when there is no overlap between the two underlying areas. The DSC is defined as followsPr and GT correspond to the set of pixels belonging to the predicted and the ground truth regions, respectively. The symbol Sensitivity (SEN) and Specificity (SPC): The SEN metric calculates the number of pixels correctly labeled as COVID-19 lesions in the predicted masks relative to the total number of pixels identified as COVID-19 abnormalities in the ground truth. The SPC metric measures the number of pixels correctly labeled as the background class relative to the total number of background pixels in the ground truth. SEN and SPC metrics are defined as followsTP, FN, TN, and FP are the number of pixels in the true positive, false negative, true negative, and false-positive regions, respectively.Mean Absolute Error (MAE), which calculates the absolute error between each pixel’s predicted and ground-truth label and takes the average over the whole pixels. The MAE is given by30. This model has been trained and evaluated on three public datasets covering six different types of lung diseases and then fine-tuned on a small COVID-19 dataset. The U-net (R231CovidWeb) can accurately extract lung regions from COVID-19 CT images and has already been used for calculating lung area in COVID-19 outcome prediction32, as a pre-processing step in COVID-19 diagnosis34, and in lesion segmentation studies36. It is worth mentioning that segmenting lung abnormalities can also be performed in an end-to-end fashion without extracting lung regions via a pre-processing step. Removing non-lung regions via a pre-processing step, however, enables the learning model to be restricted to the region of interest, reducing false-positive detection outside the lung area. Furthermore, rendering all non-lung pixels to zero decreases the computational costs and helps the model converge faster. For these reasons, several research studies with the objective of COVID-19 diagnosis or lesion segmentation from CT images have extracted lung regions in their pre-processing phase. After segmenting the lung regions, only CT images containing the lung tissues are passed to the next stage, and the rest are eliminated. Each CT image is normalized based on its mean and standard deviation (std). The datasets are divided into three independent groups for training (60%), validation (10%), and testing (30%). To avoid information leakage, we kept the underlying datasets patient-independent, meaning that patients’ CT images are not shared between training, validation, and test sets. Furthermore, to improve the model performance on unseen data and mitigate over-fitting issues, we use real-time data augmentation strategies, including zooming, shifting, and shearing, where artificial images are synthesized from each mini-batch of original CTs during the training process. The model observes each synthetic image only once, resulting in an enhancement in the model’s overall generalization ability. The network uses the Adam optimizer with an initial learning rate of 0.001 over 100 epochs and will stop if the loss function on the validation set does not decrease over ten epochs.In the pre-processing step, we extract lung regions from all CT images using a lung segmentation model, referred to as the “U-net (R231CovidWeb)”We use a combination of Datasets A and B, i.e., a total of 1784 CT images with COVID-19 manifestations from 92 patients to train/test the proposed We assess effects of different loss functions, including weighted binary cross-entropy (w-BCE), FTL, and the hybrid loss function on the model performance; results are shown in Table Group-A, CT images with infection rate smaller than 0.015, and; (ii) Group-B, CT images with the infection rate larger than 0.015. The infection rate is calculated by dividing COVID-19 lesions’ area by the lungs’ area in a CT slice. Table To better investigate our model’s performance in segmenting COVID-19 lesions at different scales, we categorize the test images into two groups: (i) As mentioned previously, in the COVID-CT-Rate dataset, we mainly focused on providing lesion masks for GGO and consolidation infection patterns, as the most frequently observed COVID-19 CT manifestations. Here, we randomly selected CT samples containing GGO and consolidation from the test set to illustrate and qualitatively discuss the model’s performance and capabilities/limitations on segmenting GGO and consolidation. As it can be observed in Fig. 37; (iii) U-Net++38, (iv) Residual U-Net with CPB module, (v) For comparative analysis, we compare the proposed 39 that has used 374 infected CT slices from Dataset C along with 100 infected CT slices for training and testing their proposed segmentation network through a five-fold cross-validation. Although their model has been trained on this dataset, the proposed When developing AI-based models using CT images, it is crucial to assess if the trained model can be generalized on CT images from a new scanner. This is mainly because CT images, acquired from different scanners with varying acquisition settings, show different resolutions and characteristics. For this purpose, we evaluate the 40, which have used 638 slices of this dataset  as an external test set for evaluation of their semi-supervised segmentation framework, the In practical settings, COVID-19 CT volumes consist of several slices, some of them with regions of infection and the rest with no evidence of infection. To assess the model’s performance in segmenting COVID-19 lesions, we test the mentclasspt{minimaTo further explore how the proposed N, 1N, 1.5N, and 2N, where N is the size of the original training set. In each fold, the validation and test sets are kept the same as the previous step of the experiment. The results of both experiments through 10-fold cross-validation are presented in Table N. Indeed, this method of generating synthetic images introduces more variability than the conventional data augmentation methods and can considerably improve the results. However, by increasing the number of synthetic images, the infection patterns with the same intensity range will be repeated during the training, causing the overfitting problem. Figure To explore the efficacy of our proposed data augmentation technique in the training process, first, we trained the 41, which have demonstrated superior capability in learning informative features from small datasets, form the basis of the developed classifier. An iterative process, known as “Routing by Agreement”, is used in capsule networks, helping identify the spatial relation between extracted features from an image. The detailed structure of the capsule-network-based module is a modified variation of the model architecture proposed in34. Specifically, the classifier adopts five convolution layers, followed by a BN layer and the ReLU activation function. Instead of the max-pooling layer, we utilize a As another variation of our COVID-19 lesion segmentation framework, as illustrated in Fig. This study is motivated by the urgent quest to develop accurate and reliable automated models for prognostic assessment of COVID-19 pneumonia coupled with the need for providing high-quality COVID-19 lesion segmentation datasets. To better position this study within the existing literature, first, we discuss the related works.Related works: Recently, several autonomous models are proposed/designed based on DL solutions to assist in the rapid diagnosis of COVID-19 from other types of respiratory infections,50. There are, however, fewer works on developing DL-based models for segmentation and quantification of COVID-19 lesions. Segmentation models designed based on DL models are mainly developed based on CNNs. Such DL models are image-to-image networks containing an encoding path for extracting high-resolution features from input images and a decoding path for generating masks indicating the regions of interest. The majority of the COVID-19 lesion segmentation models have been developed upon U-Net23 due to its superiority for the task of medical image segmentation52. For example, using a U-Net architecture integrated with DenseNet blocks in the encoder path, Chaganti et al.14 proposed a segmentation model to quantify lung abnormalities caused by COVID-19 from CT images. Similarly, Zhou et al.39 proposed an enhanced U-Net segmentation model by incorporating spatial and channel attention mechanisms. Segmentation networks can be designed based on 2D or 3D CNNs to segment COVID-19 regions of infection either on slice-level or patient-level basis. It should be noted that 3D segmentation networks that can segment infections on the whole lung volumes are more desired from practical point of view. However, they need a large amount of 3D annotated lung volumes for efficient training and are more computationally expensive. Ref.52 trained both 2D and 3D variants of the U-Net model for segmenting COVID-19 infections from chest CT images of 558 patients confirmed with COVID-19 pneumonia. According to their experiments, the 2D U-Net model achieved a Dice Similarity Coefficient (DSC) of 79.07% while 3D U-Net obtained DSC of 70.35%, demonstrating that on small datasets, slice-level segmentation can achieve better results.54. In this context, Wu et al.54 developed a joint classification and segmentation network that distinguishes COVID-19 cases from uninfected ones and at the same time produces the infection masks. They incorporated two parallel branches to extract image features for classification and segmentation tasks separately. The extracted features are then combined and fed into a decoding path to generate COVID-19 lesion masks. Performing classification and segmentation tasks simultaneously provides deep supervision for training the network on image-level and pixel-level, yielding more accurate results on both tasks. Transfer learning is a powerful technique to resolve shortage of data when deep learning models are designed for medical image analysis tasks with limited labeled datasets. Existing DL models for COVID-19 lesion segmentation have taken advantage of both natural images (like the ImageNet dataset) and medical images to enhance their model’s performance. The use of top-performing CNN models such as ResNet, DenseNet, and Inception-Net pre-trained on ImageNet dataset as the backbone of encoding path can improve the segmentation results56. For instance, Ref.57 leverages a two-stage transfer learning approach to deal with the lack of annotated data. In the first step, as the basis for their segmentation network’s encoding path, they utilize ResNet50 trained on ImageNet. The next step involves performing another transfer learning stage and pre-training the segmentation network using a large chest CT dataset for lung nodule detection. The second stage compensates for the large gap between the natural and COVID-19 chest images and enhances the final training on COVID-19 chest CT images. Some research studies use segmentation methods that require less data for training to overcome the lack of sufficient pixel-level annotated CT images. Authors in Ref.40 developed a semi-supervised segmentation framework that is trained only on 50 annotated CT images of COVID-19 patients together with 1600 unlabeled CT images. Their proposed model segments the COVID-19 lung abnormalities under both one and two classes of infections. Integrating semi-supervised and few-shot learning methods, Ref.58 introduced a segmentation network that can learn from a few number of labeled chest CT images. Authors proposed a dual-path network architecture for few-shot learning and incorporated an adaptive knowledge exchange module between the networks’ paths to enhance the model’s performance in segmenting COVID-19 lesions. Considering COVID-19 classification as a few-shot learning problem and leveraging a contrastive learning approach, Ref.59 trained an encoder that can capture discriminative feature representations from open-access datasets. The prototypical network is then adopted to detect COVID-19 cases from CT images. Alternatively, one may choose to train a DL network with weak supervision through lower-quality labels, which are inexpensive and need less time to be generated. For instance, Laradji et al.60 proposed a weakly supervised learning method by annotating a single pixel for any COVID-19 infection region on a CT image and achieved a DSC range of 68–75% on three public datasets. Their proposed labeling scheme reduced the annotation time for each infection region from 10–15 s to 1–3 s. Using some simple operations, Yao et al.61 synthesized COVID-19 regions of infection on chest CT images of healthy people and generated fake image-label pairs for training their segmentation network with no labeled data. It is worth mentioning that although these studies suggest effective solutions in compensating the lack of sufficient labeled data, they cannot yield the same success of fully supervised learning methods with accurate labels. This study aimed to address this gap.Generally speaking, to efficiently train a DL-based segmentation model, large amount of annotated CT images are required. However, due to nonuniform contrasts and irregular boundaries of COVID-19 lesions, providing pixel-level labeled datasets is challenging and expensive. Multi-task DL networks that jointly perform COVID-19 classification and lesion segmentation have shown promising results when facing lack of annotated dataTheFramework: Capitalizing on the above discussion, we proposed a deep convolutional neural network model, the socalled Several comprehensive experiments were conducted to evaluate efficacy and limitations of the proposed dy Table , it can nt Figs.  and 12 iN , improving the average DSC from 0.7624 to 0.782, the average SEN from 0.8342 to 0.8614, the average SPEC from 0.9944 to 0.9948, and the average MAE from 0.0079 to 0.0073. The experimental results indicate that concatenating the synthetic images generated by our data augmentation method will enrich the training set by introducing more variability to the training set, resulting in enhanced model performance. However, as the number of synthetic images increases, infection patterns following the same intensity range will be repeated during the training process, leading to overfitting.Experimental results show that the proposed unsupervised enhancement method can improve model’s performance. However, the external test set results showed more improvement than utilizing an extra subset acquired by the same scanner used for the initial training. The main reason is that due to prior exposure to CT images from that scanner, the model did not retain much information during the retraining process on most-certain predictions. Conversely, when adopting the proposed enhancement method on an external dataset, the model does not have any information about the unseen data from a different scanner. In this way, the model was able to acquire practical knowledge about the specifications of the unseen images through the retraining process, resulting in further improvements. As a result, the proposed unsupervised method could bridge the gap between the training and independent sets, leading to an increased generalization capability. The presented results in Tables Segmenting COVID-19 lung abnormalities under different classes of infection can yield helpful information on disease severity and stage. However, our experiments and provided CT data are limited to segmenting lung lesions under one class of data. One direction for future research is extending the dataset to model multi-class segmentation of COVID-19 lesions. Although the generalization test demonstrates that the model can work reasonably on a patient-level basis, the patients’ number in the test set is limited. More experiments on larger datasets are required to further evaluate reliability of the model patient-level application. Finally, chest CT scans can be utilized for diagnosing and assessing COVID-19 disease, however, it has been shown that standard-dose CT scanning can be highly radiation-intensive for patients, especially when multiple scans are required. In this regard, replacing low- and extra-low-dose CT scans is a safer option for the patients. Developing segmentation networks and datasets on low- and extra-low-dose CT images is another direction for future research. Segmentation models are used as the first step of severity assessment and prognosis prediction of COVID-19 patients, which would help optimize resource allocation and patient management. Future directions include extending the


Cryptococcal meningitis is a leading cause of HIV-related mortality in sub-Saharan Africa, however, screening for cryptococcal antigenemia has not been universally implemented. As a result, data concerning cryptococcal meningitis and antigenemia are sparse, and in Mozambique, the prevalence of both are unknown.We performed a retrospective analysis of routinely collected data from a point-of-care cryptococcal antigen screening program at a public hospital in Maputo, Mozambique. HIV-positive patients admitted to the emergency department underwent CD4 count testing; those with pre-defined abnormal vital signs or CD4 count ≤ 200 cells/μL received cryptococcal antigen testing and lumbar punctures if indicated. Patients with CM were admitted to the hospital and treated with liposomal amphotericin B and flucytosine; their 12-week outcomes were ascertained through review of medical records or telephone contact by program staff made in the routine course of service delivery.Among 1,795 patients screened for cryptococcal antigenemia between March 2018—March 2019, 134 (7.5%) were positive. Of patients with cryptococcal antigenemia, 96 (71.6%) were diagnosed with CM, representing 5.4% of all screened patients. Treatment outcomes were available for 87 CM patients: 24 patients (27.6%) died during induction treatment and 63 (72.4%) survived until discharge; of these, 38 (60.3%) remained in care, 9 (14.3%) died, and 16 (25.3%) were lost-to follow-up at 12 weeks.We found a high prevalence of cryptococcal antigenemia and meningitis among patients screened at an emergency department in Maputo, Mozambique. High mortality during and after induction therapy demonstrate missed opportunities for earlier detection of cryptococcal antigenemia, even as point-of-care screening and rapid assessment in an emergency room offer potential to improve outcomes. Cryptococcal meningitis (CM) is a leading cause of HIV-related mortality, causing approximately 223,000 deaths annually and also accounting for 15% of all AIDS-related deaths , while prevalence estimates were calculated as the number of patients with CrAg and CM divided by the total number of patients who underwent CrAg screening. Chi-square and Mann-Whitney tests were used to compare several HIV-related characteristics from individuals who were serum CrAg-, serum CrAg+, serum CrAg+/CSF CrAg+ and serum CrAg+/CSF CrAg-. All analyses were performed using SPSS 26.0 (IBM).a posteriori, secondary analysis of routinely collected clinical data from a de-identified dataset of an existing program, this study was deemed exempt from MSF ERB and Mozambican Ethics Review Board (ERB) review. All data were collected for purposes of patient’s follow-up and program monitoring. Confidentiality was respected and only de-identified data were used for the analysis. As such, the study did not require review by Mozambican ERB and it fulfilled the exemption criteria set by the MSF Ethics Review Board.As an During the reporting period, a total of 2,210 HIV-positive patients in the emergency department received clinical and laboratory assessments, of whom 1,795 (81.2%) met criteria for CrAg screening. The prevalence of CrAg was 7.5% (n = 134); among patients who received an LP (n = 126), 96 (76.2%) were diagnosed with CM, yielding a prevalence of 5.3% among all screened patients. Data is presented for 1,600 patients, for whom clinical data (beyond laboratory testing) was recorded.Selected characteristics and comparisons between serum CrAg+ and serum CrAg- individuals are highlighted in By CD4 count, 11.5% and 8.5% of patients with CD4 ≤ 100 cells/μL had CrAg and CM, respectively; among patients with CD4 count between 101–200 cells/μL, the prevalence of CrAg and CM were 4.5% and 2.6% . The mosComplete outcome data was available for 87 of 96 patients with cryptococcal meningitis . Among tIn the first description of its kind, we found a high prevalence of CrAg and CM among patients presenting for emergency care in Maputo, Mozambique. In-hospital and 12-week mortality were high among patients with CM. The majority of patients diagnosed with CM was already receiving ART, indicating missed opportunities for screening at the primary health level. Overall, incorporating CrAg screening into routine emergency care for patients with advanced HIV provided a feasible means for rapid diagnosis of CrAg and CM, and point-of-care testing at this hospital continues through the present.While data on the incidence and prevalence of cryptococcal disease in sub-Saharan Africa are sparse,  our estiA number of studies have further demonstrated the cost-effectiveness of CrAg screening, especially at lower CD4 counts , 19, 20.One factor hindering CrAg screening may concern limited access to therapy once CrAg or CM are diagnosed. However, oral fluconazole is becoming increasingly available worldwide, and new treatment strategies have demonstrated promise and relevance to African contexts. The cornerstone of CM treatment remains amphotericin B, and in the recent ACTA  trial, mortality was equivalent between one-week and two-week regimens of Amphotericin B . A recenEven with optimal screening and adherence to treatment protocols, CM can nevertheless be severe and often fatal. Inpatient mortality (28.7%) in our sample was high and was comparable to other recent studies in sub-Saharan Africa, including the ACTA trial  and a recent analysis of 102 cases from five South African hospitals where CrAg screening was routinely conducted , 24. In Prevention of AIDS-related deaths also relies on early and proper identification of individuals with advanced HIV, whether it is the result of late presentation or treatment failure. Owing to earlier ART initiation and increased HIV testing, the number of late presentations in Africa has been decreasing; –29 in MoUpon recognition of advanced HIV and provision of immediate care, an equally important element in the HIV care cascade involves linkage to and retention in care, which is challenging in many low-resource settings. Most studies on linkage to care are conducted in the context of treatment initiation following an initial diagnosis; barriers including distance, cost of travel, and weak ties between testing programs and treatment centres are commonly cited as difficulties for securing proper follow-up after HIV diagnosis . Of the Here, an important element of the MSF/MOH collaboration is the presence of a specialized referral center , where counsellors and physicians are specifically trained in the care of patients with advanced HIV and recently diagnosed CM. At CRAM, patients recently discharged from inpatient facilities continue receiving fluconazole, along with ongoing management of elevated intracranial pressure and/or treatment re-induction where necessary. Counselors at the Jose Macamo General Hospital and CRAM maintain regular contact in order to ensure patient arrival and follow-up. In our review, even with post-discharge care, 12-week mortality following hospital discharge (14.5%) remained high, as did the number of patients lost to follow-up (17.2%). These data highlight the significant mortality risk of CM in spite of optimal management, along with the importance of a closely monitored continuum of care from hospitalization to long-term care.Several limitations in our project should be noted. HIV testing and point-of-care screening were not performed on all eligible patients, for example those who were attended and discharged on evenings or weekend days. Thus, our prevalence estimates reflect a more ill population than would be the case if all HIV-positive patients were tested. Similarly, not all patients in the sample received TB-LAM screening for disseminated tuberculosis, and therefore, co-infection may be under-estimated among severely ill patients. Self-reported ART histories were subject to recall bias, and these data should be interpreted cautiously. Many patients were lost to follow-up, and thus true 12-week mortality is likely under-estimated. Last, many clinical records were incomplete, reflecting the difficulties of data collection in real-world settings.In conclusion, we found a high prevalence of cryptococcal meningitis among patients with advanced HIV who were screened in an emergency department of a public hospital in Maputo, Mozambique. Moreover, our implementation strategy was feasible in the context of routine clinical activities of an emergency department. CrAg screening was useful in identifying patients with CrAg and CM and provided rapid initiation of treatment, though mortality remained high among patients with CM. Further scale-up of CrAg screening, and expansion to additional hospitals along with primary health facilities, is urgently needed to further improve the care of patients with CM and advanced HIV.