During the past decades, major progress has been accomplished in establishing three-dimensional (3D) culture systems toward stem cell-derived organoids. As a significant technological breakthrough, these amazing 3D organoid constructs bridge the conventional 2D in vitro models and in vivo animal models and provide an unprecedented opportunity to investigate the complexities of veterinary diseases ranging from their pathogenesis to the prevention, therapy, or even future organ replacement strategies. In this review, we briefly discuss several definitions used in organoid research and highlight the currently known achievements in modeling veterinary diseases, including infectious and inflammatory diseases, cancers, and metabolic diseases. The applications of organoid technology in veterinary disease modeling are still in their infancy stage but the future is promising.Organoids are  One of the most significant advancements in the 3D models is the generation of organoids or ‘mini organs on a dish’ as a frontier technology  cell culture methods represent any culture of cells that recapitulates their 3D organization and/or cell–cell and cell-matrix interactions in vitro . 3D cellin vitro . When stin vitro , 11. Thein vitro , 13. Herin vitro . Alternain vitro .Ambiguities also have arisen in gastrointestinal organoid nomenclature, when mentioning “enteroids,” “colonoids” and “intestinal organoids.” Generally, “enteroids” are a type of organoid obtained from isolated intestinal crypts or stem cells of the intestine . Colonicin vitro with highly ordered architecture, they can work as powerful models for disease investigations. The widespread applications of murine and human organoid models have accelerated the development of various areas in human medicine, such as developmental and stem cell biology, disease modeling and mechanism exploration, toxicology and drug screening, and personalized medicine , and a a gondii ]. DetailRetroviridae and causes arthritis, encephalitis, and/or pneumonia in adult goats  pandemic has exhibited widespread and disastrous social impacts on humankind. Similarly, coronaviruses in veterinary medicine also cause severe economic loss in farm animals and morbidity in companion animals. Porcine respiratory coronavirus (PRCoV), a naturally occurring spike deletion mutant of highly enteropathogenic transmissible gastroenteritis virus (TGEV), could be a surrogate to study the pathogenesis of human respiratory coronaviruses. Long-term 3D porcine airway organoids (AOs) derived from basal epithelial cells and AO-derived monolayer cultures were generated and provided insights into the pathogenesis and innate immunity of PRCoV . In thesRecent publications regarding organoid applications in veterinary infectious diseases have shown a focus on gastrointestinal disease since 2019. Since the first generation of intestinal organoids of mice in 2009, intestinal organoids from intestinal crypts of humans and almost all categories of farm and companion animals  have been developed by growing research groups , 58. TheLawsonia intracellularis infection because L. intracellularis are mainly distributed in that anatomic location  or bovine rotavirus disease are mostly reported in newborn animals, so the scientists used 2- to 10-day-old specific pathogen free (SPF) piglets and colostrum-deprived neonatal calves, respectively. Ileum was used to generate organoids in the modeling of location . All entListeria monocytogenes, which exclusively invades the host via receptors on the basal side of intestinal epithelial cells even when the bacteria are on the surface of the intestinal mucosa  (in vitro models for further in-depth studies. Feline infectious peritonitis (FIP) is caused by mutated FeCoV, feline infectious peritonitis virus (FIPV)  . Fortuna24–48 h) , porcine24–48 h)  and trans (FIPV) , with twin vitro . Rabbit in vitro . Rabbit  culture . The rea culture , and it in vitro models to identify the similar intestinal epithelial response with in vivo processes after infection, including infectious tropisms of specific intestinal segments and cell types, cytokine, and inflammatory responses in the gene transcriptions. Toxoplasma gondii is one of the most ubiquitous parasites and can infect almost all homoeothermic species including humans and livestock, being present in all countries  is one of the important causes of postweaning diarrhea in piglets which results in increased mortality, reduced growth rates, and severe economic losses in swine husbandry  identifies a spontaneously occurring group of chronic idiopathic enteropathies leading to serious debilitating inflammation of the gastrointestinal tract . Like IBin situ hybridization (ISH) probe  probe . That miH) probe . LPS tre4.In cancer research, organoids have represented promising, near-physiological models of human cancers. Up to now, numerous robust and efficient establishment of human organoids can be achieved from various types of neoplasms arising from lung, breast, stomach, liver, pancreas, kidneys, urinary bladder, and prostate . In contOrganoid models of animal cancer, which might be derived from a small number of stem cells, enabled a better understanding of the molecular characteristics and neoplastic behavior of the disease in animals and acted as preliminary models for drug screening –81. For Due to better preservation of tumor microenvironment than conventional 2D cell line models, organoids of canine prostate cancer , canine in vitro drug assays with reproducible dose–response and expected dose-dependent tolerance. For example, after initial molecular characterization of established organoids, several genes could be found to be specifically upregulated, which could also be potential targets for novel therapies, as conducted in studying canine bladder cancer and canine lung cancer  might largely be due to their similarities in enhanced cellular interactions via adhesion and secretion of soluble factors of tumors  and discuss the feasibility of modeling the corresponding human diseases.−/− dogs had a higher intracellular copper accumulation after being subjected to high copper levels for 3 h. This finding demonstrated liver organoids established from the dogs with an autosomal recessive COMMD1 deficiency maintain the defect of copper excretion, similar to the situation in the in vivo situation, and supported the feasibility of using diseased canine hepatic organoids to model the copper storage disease. Gene correction was performed on COMMD1-organoids by using lentiviral vectors bearing the COMMD1 gene. After transduction, COMMD1 gene supplementation normalized cellular copper content in the organoids to wild-type levels within 24 h evidenced by the copper level comparison after 3- or 24-h copper treatment with differentiated COMMD1−/− and wild-type organoids. The authors indicated that these results confirm that organoids from canine liver diseases serve as a robust translational model for liver diseases such as Wilson’s disease and illustrated the amazing therapeutic potential for correcting genetic errors when combined with genome editing technology. In a follow-up study  gene lead to an autosomal recessive copper toxicosis associated with defective biliary excretion of copper resulting in massive hepatic copper accumulation and displaying many hallmarks of Wilson’s disease. The potential of hepatic organoid technology to address copper storage disease in the liver has been investigated in the COMMD1-deficient canine model recently . 14-day up study , the samFeline hepatic steatosis (FHS), one of the most common hepatobiliary diseases in cats, is characterized by triglycerides (TGs) accumulation in most of the hepatocytes, leading to significant hepatomegaly, impairment of liver function, and intrahepatic cholestasis. The pathophysiology of FHS is complex. Several similarities between feline and human steatosis and the unique sensitivity of this disease in cats encouraged scientists to establish a long-term feline model that might mimic Non-Alcoholic Fatty Liver Disease (NAFLD) and assess the efficacy of potential drugs for the treatment of FHS , 52. A s6.in vitro applications in veterinary diseases. Compared with the number of generated veterinary organoid models from normal tissues, the applications of these organoids to model diseases are currently relatively scarce. For example, generating organoids for reproductive organs  and embryoids made a lot of progress in farm animal species , studying intricate receptor-ligand interactions for the infectious disease and malignant transformation . As talkOrganoids assume a pivotal role within the domain of precision medicine. Some diseases, like cancer and IBD, are heterogeneous and include complex interactions of diverse cellular or non-cellular components. Organoids are cultivated utilizing pathological tissues procured from afflicted patients with features of micro-environments. Patient-derived organoids (PDOs) work as a tool for informed personalized medical determinations, with the capacity to anticipate patients’ reactions to therapeutic protocols and, in turn, foster the prospect of ameliorated treatment efficacy. In addition to personalized therapies, the establishment of a well-defined “living organoid biobank” for multifactorial diseases is gaining increasing attention. The critical advantage is to provide more accurate information of inherent intricacies of these diseases after large-scale sequencing and drug screening and more faithfully mirror patients’ receptivity to pharmaceutical agents and their capacity to endure drug-induced toxicities . As the in vitro tool with relevance to drug discovery and development . ASC-derived organoids have greater differentiation features, but they solely consist of cells of a single lineage and do not have dynamic attributes and advanced functionalities of authentic organs, despite this arrangement offering the advantage of directly manifesting the impact of experimental treatments on specific target cells. However, the complexity of multicellular and dynamic organs always conceals more intricate mechanisms of diseases, and tumor microenvironments also play a crucial role in mediating some of the effects of chemotherapy and radiotherapy , so reinin vivo organs. For example, the hepatic organoid exhibits the expression of markers associated with hepatic progenitor cells and biliary cells but without the mature hepatocyte marker HepPar-1 (in vivo better than a highly stereotypic limited response. Standardization is an important step to ensure results obtained from organoid models are consistent, reproducible, and comparable. While there has been limited focus on standardization within veterinary domains, a publication has addressed this gap by establishing standardized protocols for maintaining 3D canine hepatic and intestinal organoid cultures (Admittedly, besides the reduction in complexity of current organoid models and its static nature, other significant limitations are also noticeable . First, HepPar-1 . OrganoiHepPar-1 –36, 56. HepPar-1 . For a mHepPar-1 . These pcultures . This efin vitro models, such as primary cells or tissue explants, have a finite replication capacity and rapid senesce (Although organoid models are relatively expensive and cannot model the whole organism as an animal model does, they still cheaper and easier to manage than animal models and enable scientists to collect data quickly, while avoiding the ethical issues involved in using animals for research . Modelin senesce .in vitro models for studying disease pathogenesis and drug development. The future of organoid technology in veterinary disease modeling is promising.To conclude, organoid culture offers more than conventional 2D cell culture systems do in basic and applied research. The current application of organoids in veterinary diseases is in its infancy but developing quickly. Organoid models have a huge potential to be durable BC wrote the first draft of the manuscript. RS and SG contributed to manuscript revision, edited, and approved the submitted version. All authors contributed to the article and approved the submitted version.


Emergency departments (ED) provide trauma-informed care to sexual assault (SA) survivors and connect them with comprehensive services. Through surveying SA survivor advocates, we aimed to 1) document updated trends in the quality of care and resources offered to SA survivors and 2) identify potential disparities according to geographic regions in the US, urban vs rural clinic locations, and the availability of sexual assault nurse examiners (SANE).We conducted a cross-sectional study between June–August 2021, surveying SA advocates who were dispatched from rape crisis centers to support survivors during ED care. Survey questions addressed two major themes in quality of care: staff preparedness to provide trauma-response care; and available resources. Staff preparedness to provide trauma-informed care was assessed through observations of staff behaviors. We used Wilcoxon rank-sum and Kruskal-Wallis tests to analyze differences in responses according to geographic regions and SANE presence.A total of 315 advocates from 99 crisis centers completed the survey. The survey had a participation rate of 88.7% and a completion rate of 87.9%. Advocates who indicated that a higher proportion of their cases were attended by SANEs were more likely to report higher rates of trauma-informed staff behaviors. For example, the recalled rate of staff asking patients for consent at every step of the exam was significantly associated with SANE presence (P < 0.001). With respect to access to resources, 66.7% of advocates reported that hospitals often or always have evidence collection kits available; 30.6% reported that resources such as transportation and housing are often or always available, and 55.3% reported that SANEs are often or always part of the care team. The SANEs were reported to be more frequently available in the Southwest than in other US regions (P < 0.001) and in urban as opposed to rural areas (P < 0.001).Our study indicates that support from sexual assault nurse examiners is highly associated with trauma-informed staff behaviors and comprehensive resources. Urban-rural and regional disparities exist regarding access to SANEs, suggesting that elevating nationwide quality and equity in care of survivors of sexual assault requires increased investments in SANE training and coverage. Sexual assault (SA) is a nationwide public health crisis with long-term health consequences. Within the United States, 43.6% of women and 24.8% of men experience some form of contact sexual violence within their lifetime.2Emergency departments (ED) play a critical role in serving the approximately 21% of SA survivors who seek acute medical attention.10Many EDs use additional support from specialized sexual assault response teams when caring for SA survivors. These teams typically consist of SA patient advocates and/or sexual assault nurse examiners (SANE). The SA patient advocates serve as first-response crisis counselors, assist survivors in navigating the medical and legal processes in the ED, and provide referrals to follow-up support services.16Providing SA survivors with high-quality care can be challenging for many hospitals. A 2013 survey of US hospitals found that only ~20% provided survivors with comprehensive services, including SA crisis counseling, sexually transmitted infection management, HIV management, and emergency contraception.22What do we already know about this issue?Unites States’ emergency departments provide post-assault care to over 55,000 sexual assault (SA) survivors per year. The quality of trauma-informed care and resources offered are highly variable.What was the research question?Are there disparities in the care offered to SA survivors according to geography ?What was the major finding of the study?The availability of SA nurse examiners (SANE), which is lower in rural compared to urban areas (P<0.001), is positively associated with trauma-informed care (P<0.001) to the benefit of SA patients.How does this improve population health?Elevating nationwide quality and equity in SA survivor care requires increased investments in SANE training and coverage.When studying nationwide trends and potential disparities in ED care of SA survivors, patient advocates can serve as reliable sources of information.Our methods are reported according to the Checklist for Reporting Results of Internet E-Surveys (CHERRIES).23We developed an electronic survey to explore two major themes in ED preparedness in caring for SA patients: staff preparedness and physical resources. Staff preparedness to provide trauma-informed care was assessed through advocates’ observations of staff behaviors that previous studies have identified as potentially retraumatizing, such as expressing disbelief or blame and not providing thorough explanations of care.Patient advocates for SA victims were recruited from participating rape crisis centers via email. We identified participating rape crisis centers via online search and contacted them via phone and email. Of 137 centers with advocacy services where study team members spoke directly to center staff, 135 agreed to distribute the survey to their SA patient advocates. After agreeing to assist with survey distribution, staff at participating centers sent the survey link and background information to their SA patient advocates via email. Before providing consent via an online survey form, participating SA patient advocates were provided information about the research aims, study time commitment, privacy risks, and investigator contact information. Participants were offered the opportunity to enter a raffle for a $250 gift card as a survey incentive.Survey responses were captured automatically via the secure REDcap platform hosted at University of Chicago between June–August 2021. Survey data were stored separately from identifiable participant data that was collected for recruitment purposes. The survey included between 57–100 items (dependent on adaptive questioning) distributed over four pages. Survey respondents were able to review and change their answers using a back button. Surveys that were terminated early were included in the analysis.P-values to control the false discovery rate were computed using the Benjamini-Hochberg method; an adjusted P-value of < 0.05 was considered significant.To analyze differences in survey responses between geographic regions in the US and between urban and rural clinic locations, we first coded Likert-type survey responses on a five-point ordinal scale. Data from the US Department of Agriculture were used to classify the county within which rape crisis centers were located as urban or rural.The survey had a participation rate  of 88.7% and a completion rate  of 87.9%. A total of 321 advocates from 119 crisis centers responded to the survey. Crisis centers represented 44 states and ranged from rural crisis centers serving numerous counties to crisis centers affiliated with urban, academic medical centers. Participant demographic information is summarized in P < 0.001).There were no significant differences in quality-of-care indicators related to provider attitudes and behaviors between US geographical regions or urban vs. rural regions. However, advocates who indicated that a higher proportion of their cases were attended by SANEs were more likely to report higher rates of trauma-informed staff behaviors. Notably, the recalled rate of ED staff explaining all medical care and asking patients for consent at every step of the exam was significantly associated with SANE presence  reported that ED staff were never or rarely comfortable completing a medical forensic exam. While most hospitals have the resources to conduct forensic medical examinations, resources to meet survivors’ comprehensive needs are less consistent. For example, while 78.8% of advocates recalled that hospitals sometimes, often, or always have SA evidence collection kits available, only 57.9% of advocates recalled that hospitals sometimes, often, or always have resources to support patients after discharge, such as clothes for patients to change into, vouchers for follow-up care, and information to address survivors’ basic needs, such as transportation and emergency housing. Nearly three-quarters (74.4%) of advocates reported that the patient care team was sometimes, often, or always supported by a SANE.P < 0.001). SANEs were also more frequently present in urban as opposed to rural areas (P <0.001). Advocates who indicated that a higher proportion of their cases were attended by SANEs were more likely to report higher rates of several components of hospital preparedness, including shorter waiting times, lower rates of survivors repeating their story, ED staff comfort with the medical exam, availability of forensic exam kits, and availability of follow-up resources (P <0.001) and the availability of post-discharge resources (P< 0.001).With respect to differences in hospital preparedness among both US geographical regions and urban vs rural regions, SANEs were more often part of the care team in the Southwest than in other US regions (esources . SANE prEvolving societal perceptions of SA have changed the ED care of SA survivors, with increased ED utilization and advancing standards for trauma-informed care.Our findings on quality-of-care indicators related to clinician attitudes and behaviors, including levels of conveyed disbelief and blame, indicate that SA survivors who present to hospitals nationwide may be exposed to retraumatizing interactions. This study provides a nationwide perspective on the prevalence of negative interactions between SA survivors and ED staff that have been previously documented in local or regional qualitative studies.The association of SANE presence with various survey measures of high-quality care aligns with prior studies documenting that EDs with SANE programs provide comprehensive medical services and proper completion of forensic examinations at higher rates than EDs lacking SANEs.40Although SA is understudied in rural areas, our data aligns with studies documenting scarce resources, including healthcare personnel, in specific rural areas.47Addressing these urban vs rural disparities in SA survivor care requires the implementation of evidence-based strategies to recruit, train, and retain SANEs to serve rural regions. Innovative training programs developed through the Advanced Nursing Education - Sexual Assault Nurse Examiners Program have proved successful in improving SANE coverage in regions of Texas and Florida and can serve as a model for widescale SANE-coverage expansion.49While expanding educational opportunities for SANE training is a foundational step, it is merely one component of many necessary steps to reduce disparities and elevate the quality of SA survivor care nationwide. Our study and previous work show that notable proportions of non-SANE ED staff may be uncomfortable with performing the medical forensic exam. This also provides a wider context to qualitative studies documenting low self-efficacy among ED staff when working with SA patients and demonstrates that insufficient training in SA patient care is a problem on a national level.54The inherent limitations of this study should inform interpretation of our data. Advocates who work with centers that dispatch SA advocates to multiple hospitals reported an average of their experiences. Therefore, granularity regarding hospital type was lost. Additionally, most survey respondents were White, cisgender women. While this is likely reflective of the nationwide population of SA patient advocates, it is not reflective of SA survivors themselves and, therefore, could have influenced the survey data obtained. Future studies should further explore disparities in quality of ED care offered to SA survivors that may be influenced by patient identity. Additionally, survey respondents may have been influenced by recall bias and thus may have reported the more memorable interactions with emergency clinicians.Our study underscores the importance of more consistent standards for hospital preparedness to elevate the nationwide quality of ED care of sexual assault patients. Interventions should aim to decrease ED wait times, reduce the number of times patients must repeat their stories, and improve the consistency with which post-discharge resources are offered to patients. Addressing gaps in staff preparedness through more robust clinician training and increased consistency in coverage by sexual assault nurse examiners should also be prioritized to minimize potentially retraumatizing experiences for SA patients in the ED. This is particularly important to address disparities in the quality of care offered to urban and rural sexual assault survivors.


Finally, the capabilities and the limitations of the nanoscaletechniques are discussed.We investigate the role of grain structures in nanoscalecarrierdynamics of polycrystalline solar cells. By using Kelvin probe forcemicroscopy (KPFM) and near-field scanning photocurrent microscopy(NSPM) techniques, we characterize nanoscopic photovoltage and photocurrentpatterns of inorganic CdTe and organic–inorganic hybrid perovskitesolar cells. For CdTe solar cells, we analyze the nanoscale electricpower patterns that are created by correlating nanoscale photovoltageand photocurrent maps on the same location. Distinct relations betweenthe sample preparation conditions and the nanoscale photovoltaic propertiesof microscopic CdTe grain structures are observed. The same techniquesare applied for characterization of a perovskite solar cell. It isfound that a moderate amount of PbI As a promising example, the cellsbased on new emerging materials such as perovskite solar cells arerapidly improving with the best cell efficiency greater than ≈26%,28 exceeding the performance of traditional solarcell technologies. A significant enhancement of optical and electricalproperties of solar cells has also been achieved with nanophotonicmaterials, such as metallic particles,22 dielectric scatterers,27 and semiconductor nanostructures.26As traditional energy sources are continuouslyused up and/or pollutingthe environment, it is imperative to develop next-generation renewableenergy sources, including solar cells. While it will take time forsolar cells to fully replace or significantly supplement the conventionalenergy options, relentless efforts are being made to improve costefficiency of solar cells. Considerable recent progress driven bymaterial developmentAs solartechnologies are gradually improving, it is essentialto develop new characterization techniques to advance our understandingof newly developed cells. In-depth understanding of cell operationacts as a foundation for further improving photovoltaic technologies.To thoroughly analyze the photovoltaic characteristics, we need toinvestigate the fundamental interaction between photons, electrons,and materials at the nanoscale because photogenerated carrier generationsand collections take place on this scale. This brings forward theneed of developing nanoscale characterization techniques.41 The techniques differ in the light injection and carrier collectionmechanisms. For example, photoconductive AFM injects light over arelatively large sample surface area and collects photogenerated carriersinto a conductive AFM cantilever.40 Here, we use near-field scanningphotocurrent microscopy (NSPM) that utilizes a small tapered opticalfiber as a local light source and collects photogenerated carriersthrough macroscale sample electrodes -based methodshave been developed for nanoscale photocurrent measurements.odes see 1.34,38,346 In this work, we use Kelvin probe force microscopy (KPFM), one ofthe most sensitive scanning probe techniques, to measure the photoinducedsurface contact potential of solar cell devices having nanoscale spatialresolution. In KPFM measurements, an AFM cantilever detects the contactpotential difference (VCPD) of a sampleby detecting the potential offset between a probe tip  and sample surface  solar cells and organic–inorganic hybrid perovskitesolar cells. We found that the spatial patterns of both quantitiesdepend sensitively on the processing/fabrication history with thecontrast between the grain cores and grain boundaries being reversedupon treatment and/or opposite for photocurrent and photovoltage.As a result, a single type of measurement appears to be an insufficientpredictor of the cell efficiency and/or usefulness of a particularprocessing condition. Thus, we also determined nanoscopic local electricpower patterns of CdTe solar cells by combining nanoscale photovoltageand photocurrent images on the same location. Finally, we discussthe capabilities and the limitations of the nanoscale measurementtechniques used in this study.Customized tuning fork-based atomic force microscopy (AFM) probeswere used in this study. A tapered optical fiber having a small aperture(a diameter of around 150–250 nm) was used to locally injectlight. The optical fiber was coupled with a diode laser (a wavelengthof ≈520 nm). The measured electrical signal was amplified bya preamplifier and was low-pass-filtered using a lock-in amplifierto increase the signal-to-noise ratio. While in noncontact mode rasterscanning, both topography and photocurrent images were acquired fromthe same location of the sample.In tapping modeAFM, a platinum iridium (Pt/Ir)-coated cantilever  was used to simultaneously acquireboth topography and surface potential images. The cantilever was connectedto the rear contact of a sample to eliminate any charging effect ofthe sample. To illuminate the sample, we used a bench top laser. AFC/PC fiber was connected to the output port of the laser, and theother end was connected with a collimator for uniform light illumination.2 solution at 400°for 25 min. A gold layer was sputtered using a DC magnetron sputteringprocess to form a contact having a thickness of 130 nm.CdTe thin-film cells composedof CdS and CdTe layers were deposited on a fluorine-doped tin oxide(FTO)-coated float glass substrate. Prior to the deposition process,the substrates were heated up to two different temperatures of 310and 500 °C, referred to low-temperature (LT) and high-temperature(HT) in this work, respectively. The average thickness of the CdTeand CdS layers are 4–5 μm and ≈150 nm, respectively.After the CdTe deposition, Cl activation treatment was carried outusing a saturated methanolic CdCl2 and methylammonium iodide with a 1:1 molar ratiowas dissolved in a mixture of N,N-dimethylformamide and dimethyl sulfoxide (DMSO) (9:1 by volume)and was spin-coated at 4000 rpm for 25 s. Antisolvent (diethyl ether)was quickly dropped on the substrate during the spinning process toform PbI2-MAI-DMSO intermediate phases. For crystallizationof perovskite, the substrate was annealed at 100 °C for 10 min.The electron transport layer composed of a 50 nm thick zinc oxidenanoparticle (ZnO NP) layer and a 50 nm thick phenyl-C61-butyric acid methyl ester ([60]PCBM) layer was added. The PCBM layerwas formed by spinning of the -phenyl-C61-butyricacid methyl ester solution (20 mg/mL in chlorobenzene) on top of theperovskite layer. Finally, a 120 nm thick Ag electrode was depositedby thermal evaporation.An indium tin oxide (ITO)layer on a glass substrate was rinsed by sonification in water, acetone,and ethyl alcohol in sequence. A solution containing a 1 mmol of nickel(II)acetate tetrahydrate in ethyl alcohol was spin-coated on the substrateat 4000 rpm for 45 s, followed by annealing at 300 °C for 1 h.The PbI41 The motivation for investigating CdTe solar cells withdifferent substrate temperatures is to lower the energy budget ofthe fabrication process. The third cell is an optimized, commerciallyavailable CdTe solar cell, and the Cl activation treatment was alsoapplied to this sample. This set of samples was used to study therole of microscopic CdTe grain structures and to find the correlationbetween macroscale photovoltaic properties and nanoscale photoresponses.We examined three differenttypes of CdTe cells. Two differentCdTe cells with p-type CdTe and n-type cadmium sulfide (CdS) werefabricated specifically for this project. During the fabrication,the cell substrates were annealed at different temperatures. A low-temperature(LT) sample and a high-temperature (HT) sample were heated at 310and 500 °C, respectively, during the deposition of CdTe layer.Chlorine (Cl) activation treatment was subsequently carried out toreduce the recombination centers at grain boundaries.2, while the open-circuitvoltage is increased from 760 to 800 mV. We attribute these changesto the enlarged average grain size of the HT sample.41 While forming larger grains, the density of microscopiccarrier recombination sites  is reduced, andtherefore, the open-circuit voltage of the entire sample increases.48 It is found that the average grain size of the LT sample is lessthan ≈1 μm and the HT sample is greater than ≈3μm results]. The Cl segregation at grain boundaries in the HT CdTe samplehinders effective carrier collections at grain boundaries, reducingthe short-circuit density of the device. The trade-off between theshunt loss and the short-circuit density explains why the optimizedsample achieved the best cell efficiency even though its fill factorwas not the highest among all samples.When we compare the HT sample tothe optimized one, the HT sampleexhibits the lower power conversion efficiency despite the fact thatthe fill factor of the sample is higher. The higher fill factor inthe HT CdTe cell can be attributed to its larger grain size comparedto the optimized sample, originating from the elevated annealing temperature.The larger grain effectively decreases the density of grain boundaries,and thus, the shunt loss of the device is suppressed. However, theHT CdTe cell has a lower short-circuit current density compared toother samples due to Cl segregation at grain boundaries originatedfrom the higher annealing temperature PCBM) film. A 120 nm Ag layerand a 280 nm indium tin oxide (ITO) layer form contacts to the electrontransport layer and the hole transport layer, respectively. The wholecell structure was fabricated atop a soda-lime glass substrate. Whilesome grain boundaries show enhanced photovoltage, we also measuredhigh photovoltage at some other grain interiors as shown in 2), local compositional variations, and different degrees ofpassivation at grain boundaries and grain interiors.Both KPFM and NSPM measurements can be readilyused for characterizationof solar cells made from different materials, including organic–inorganichybrid perovskite solar cells. To prove this, the same measurementtechniques were applied to examine the local photoresponse of a hybridperovskite solar cell. A 400 nm thick methylammonium lead iodide (CH39 A certain amount of segregated lead iodide(PbI2) layers effectively acts as a barrier for holes.39 The barrier prevents holes from facing shallowcarrier traps located near grain boundaries that are not passivated,ultimately reducing carrier recombination. Thus, the moderately segregatedPbI2 layer near grain boundaries is the driving force totransport more photogenerated minority carriers at the grain boundaries.60 Furthermore, lead (Pb) in grain interiors and PbI2 ingrain boundaries share the same coordination, so the existence ofPbI2 near grain boundaries helps reduce the concentrationof dangling bonds at the grain boundaries.39 It is worth mentioning that photogenerated carrier collections aresignificantly suppressed along grain boundaries if an excessive amountof PbI2 is segregated and stacked at grain boundaries  images).Unlike the varying photovoltage distribution patterns,it is foundthat photocurrent is consistently enhanced near grain boundaries see 6f. This 39 As our nanoscale scans were accomplished ina relatively very short period of time, the dynamics of PbI2  was very limited in time and space.Furthermore, it is important to note that both KPFM and NSPM measurementsare basically nondestructive measurement techniques. Nonetheless,to minimize any effects from light-induced degradation or PbI2 redistribution under light illumination for our KPFM andNSPM measurements and to fairly quantify and compare two differentnanoscale electrical properties ,KPFM and NSPM measurements were performed on two different areas whereno prior light-induced measurements were carried out. This approachhelps us to ensure that the PbI2 content remains relativelyconsistent during all measurements, allowing for a more accurate evaluationof nanoscale optoelectronic responses of a perovskite solar cell.In contrast to inorganicCdTe solar cells, the organic–inorganichybrid perovskite solar cell we used for our study showed some degreeof performance degradation under continuous light illumination. However,no significant compositional changes were found until ≈400min of continuous light illumination under the AM1.5G spectrum.2 near grain boundaries in the perovskite solar cell can have a considerableimpact on photovoltage distribution. As already discussed, the existenceof PbI2 near grain boundaries can effectively reduce photogeneratedcarrier recombination and alter the photovoltage distribution acrossthe scanned area.The heterogeneity in photovoltage across the scanned area of theorganic–inorganic hybrid perovskite cell can be attributedto variation in material properties such as grain structures, defectdistributions, and others. Perovskite materials exhibit a high degreeof structural complexity and compositional variability, and therefore,the grain structure of perovskite solar cells can be significantlyinconsistent. For example, the presence of segregated PbIWhile we have demonstrated the capabilitiesof combining and correlatingtwo AFM-based methods, KPFM and NSPM, the limitations of the measurementtechniques should also be discussed. Surface contact potential informationobtained by KPFM measurements does not necessarily represent the bulkoptoelectronic properties and may be limited to the response of thesurface region of samples. At worst, the contact potential would merelycharacterize the surface recombination, if unavoidable surface defectstates are extremely high. Useful information regarding the localcarrier collection and loss within the cells can be hidden in theoptoelectronic response that represents the surface defect states.Thus, a further work to separate meaningful contact potential valuesfrom the surface recombination information is required. In addition,as the KPFM and NSPM measurement techniques use different light sourcesand methods, the obtained photoresponse may not be directly linkedto quantify the nanoscale power conversion efficiency. While the NSPMtechnique is based on a local area light injection  and a large area electrical propertydetection mechanism , the KPFM technique injectslight onto a relatively large area of the sample surface and detects electrical properties at a local area using a conductivecantilever. We note that the current–voltage measurements accomplishedeither by using a local contact of the order of the grain size orby photoconductive AFM cannot reliably represent the local power efficiency.In such measurements, the local contact collects photoexcited carriersgenerated over the area that is larger or much larger than the contactsize depending on the photovoltaic technology and in-plane transportproperties of photoexcited carriers. Thus, the approach describedin the present paper represents the best approximation of the localefficiency in spite of the limitations discussed above.Despitesuch limitations, this work shows the capability to comprehensivelyevaluate the role of grain boundaries in polycrystalline thin-filmsolar cells by correlating multiple nanoscale electrical properties.With further advancing nanoscale measurement techniques, developingmultiple measurement techniques relying on similar carrier generation/collectionmechanisms, and performing proper correlations, the multiple datasets can be used to accurately evaluate how the optical, electrical,and material factors affect nanoscopic electrical power generationand loss. This information is useful in determining the macroscopicbehavior of solar cells, leading to a more in-depth interpretationof device operations, and eventually contributing to building an accuratemacroscale operational model of polycrystalline solar cells.2 efficiently collect photogenerated minority carriers.We investigated nanoscale optoelectronicresponses of CdTe andperovskite solar cells using KPFM and NSPM measurements. We observedthat grain boundaries of CdTe solar cells can simultaneously act bothas carrier recombination centers and efficient carrier collectors.Grain boundaries are photogenerated minority carrier collectors dueto the electric field, explaining the increased photocurrent neargrain boundaries. However, they simultaneously also work as recombinationcenters due to the inherent surface detects and dangling bonds betweenthe grains. Thus, we provided a map of nanoscale power, which incorporatesboth the photocurrent and photovoltage to evaluate how grain boundariesaffect the power efficiency of the cells. We also found the correlationsbetween the nanoscale and the macroscale CdTe photovoltaic propertiesbased upon our measurements. It is found that the nanoscale informationcan support macroscale data sets, providing a path for an accuratemacroscale polycrystalline solar cell operational model based on nanoscaleinformation. For a perovskite solar cell, the beneficial role of graininteriors and grain boundaries based on the nanoscale photovoltageimage is equivocal. However, based on the nanoscale photocurrent image,it is clear that the grain boundaries with a moderate amount of PbI


A chronic expanding hematoma (CEH) is a rare clinicopathologic entity that may simulate the clinical and radiologic presentation of soft tissue sarcomas. Etiology has been attributed to repeated exudation and bleeding from capillaries in granulation tissue, resulting in a gradually enlarging mass. A 51-year-old male presented with a large thigh mass following a gunshot wound one year prior. Diagnostic imaging revealed a large complex mass with cystic areas overlying cortical erosions in the femoral diaphysis suggestive of osteomyelitis versus a primary aggressive new growth. Biopsy confirmed CEH and the absence of malignant cells. Hip disarticulation was performed after noting massive necrosis of the thigh compartments and neurovascular compromise. CEH is an important differential diagnosis to be considered in a patient with a slow-growing soft tissue mass and history of significant trauma. Its similar clinical presentation with a soft tissue sarcoma necessitates a high index of suspicion, diagnostic imaging, and biopsy prior to performing definitive surgery. Subsequent accounts of CEH are case reports and small case series, and the authors surmise its rarity may be due to underreporting of such cases. CEH are commonly found in the abdomen and pelvis, while those of the lower extremity are less common2 possibly owing to less potential space for expansion.Chronic expanding hematomas (CEH) were first described as a clinicopathologic entity in 1980 by Reid 2. Clinically, both present as a slow-growing mass2. On magnetic resonance imaging (MRI), CEH mimic neoplasms by presenting as a central mass of blood surrounded by a wall of dense fibrous tissue1. A biopsy is necessary to establish a diagnosis followed by appropriate surgical management2.This poses a diagnostic dilemma due to its similarities to soft tissue sarcomas (STS)Here, we present a rare case of a CEH occurring in the thigh of an adult male, initially considered to be a soft tissue sarcoma.A 51-year-old male sought consult due to a left thigh mass. The patient sustained a gunshot wound to the thigh one year prior, for which he was treated with debridement. Two months after surgery, patient remained non-ambulatory with progressive enlargement of the left thigh which prompted consult.Examination revealed a tense, warm, and tender mass measuring 73.5cm in circumference . Left hiUltrasound revealed a non-compressible mass with areas suggestive of necrosis. Computerised tomography (CT) scans  showed aA sono-guided core needle biopsy revealed necrotic tissues admixed with blood and fibrin. A subsequent open biopsy was consistent with fibroblastic proliferation, chronic osteomyelitis, focal foreign-body giant cell reaction, and no overt evidence of malignancy . PatientSpecimen cultures revealed growth of Escherichia coli, treated with culture-guided antibiotics. Duplex scans showed 50-99% stenosis of the external iliac arteries, as well as acute deep vein thrombosis of the external iliac and common femoral veins. An electrophysiological study of the affected extremity revealed severe axonal lumbosacral (L2-S1) plexopathy.Due to massive necrosis of the anterior and medial thigh compartment, destruction of the femur, chronic infection, and neurovascular involvement, ablative surgery was recommended. The patient consented and underwent hip disarticulation. Two years post-operatively, the patient is well and without further complaints.et al in 1980, five presented with a slowly expanding truncal masses, while only one patient was described as having a mass on the lower extremity1. Subsequent reports5 describing lower extremity CEH consisted of similar patients presenting with soft tissue masses that resembled an STS including synovial sarcoma, epithelioid sarcoma, extraskeletal Ewing sarcoma, and undifferentiated pleomorphic sarcoma. Similarly, this case presented with a gradual enlarging thigh mass which appeared as a soft tissue sarcoma, prompting comprehensive diagnostic examination.In a series of six patients by Reid 1. Histologically, CEH presents with a peripheral zone of collagenous tissue containing hemosiderin-laden macrophages, and multinucleated, foreign body-type, giant cells4. A midzone has also been observed, containing loose connective tissue fibrin and predominantly amorphous debris, often with capillary ingrowth4.Despite variable locations, gross appearance from all cases consisted of an outer wall of fibrous tissue and a central collection of blood1. Bleeding from injury causes hematoma formation1. The breakdown of blood within the hematoma activates vasoactive substances that induce capsule formation, while propagating inflammatory cascade resulting in increased vascular permeability and expansion of the hematoma5. Another theory is the presence of vascular endothelial growth factor (VEGF). Activation of VEGF results in angiogenesis, leading to repeated exudation of blood from newly formed capillaries5. Chronic increased pressure within a closed compartment was theorised to lead to compression of adjacent muscles, impeding blood flow and causing rhabdomyolysis followed by subsequent necrosis1.An inflammatory cascade is considered in the aetiology of CEH5. MRI remains the modality of choice and may show a “mosaic sign” which are multiple signal intensities within the lesion corresponding to blood products of different ages5. Musculoskeletal ultrasound will show a multilocular cystic appearance5.Evaluation of CEH with CT scans appear multicystic with varied attenuation within the lesion, with or without a partially calcified rim5.Biopsy with adequate specimens should be done to establish a diagnosis prior to any surgical management. Histologic sections of CEH consist of a mixture of necrotic debris, fibrin, blood clots, cholesterin crystals, and histiocytes, without evidence of malignant cells5. Incomplete excision has been reported to cause further bleeding from a hypervascular subcapsular lesion5. Aspiration is not enough to remove the contents of the hematoma and will not remove the fibrous capsule5. In this reported case however, the chronicity of disease lead to massive muscle necrosis of the thigh compartments and bony destruction of the femur compounded with osteomyelitis, hence ablative surgery was recommended. This reiterates individualised plan of management for all patients following completion of thorough and complete investigation.While treatment guidelines for CEH are not established, complete surgical excision including the pseudocapsule is the current recommendation of choiceIn the presence of a patient with a chronically enlarging extremity mass and a history of trauma, CEH should be considered as a differential diagnosis. With a high index of suspicion, proper imaging modalities can help detect these pseudotumours. Tissue biopsy is necessary to confirm histopathologic diagnosis prior to management.


Deep learning has become a leading subset of machine learning and has been successfully employed in diverse areas, ranging from natural language processing to medical image analysis. In medical imaging, researchers have progressively turned towards multi-center neuroimaging studies to address complex questions in neuroscience, leveraging larger sample sizes and aiming to enhance the accuracy of deep learning models. However, variations in image pixel/voxel characteristics can arise between centers due to factors including differences in magnetic resonance imaging scanners. Such variations create challenges, particularly inconsistent performance in machine learning-based approaches, often referred to as domain shift, where the trained models fail to achieve satisfactory or improved results when confronted with dissimilar test data. This study analyzes the performance of multiple disease classification tasks using multi-center MRI data obtained from three widely used scanner manufacturers  across several deep learning-based networks. Furthermore, we investigate the efficacy of mitigating scanner vendor effects using ComBat-based harmonization techniques when applied to multi-center datasets of 3D structural MR images. Our experimental results reveal a substantial decline in classification performance when models trained on one type of scanner manufacturer are tested with data from different manufacturers. Moreover, despite applying ComBat-based harmonization, the harmonized images do not demonstrate any noticeable performance enhancement for disease classification tasks. A prominent ML branch known as deep learning (DL) builds models using layers of interconnected neurons to learn critical insights from existing data and to predict the outcome for new data. Unlike traditional ML methods, DL networks automate feature extraction and selection, making them user-friendly and more prevalent than classical ML techniques. Recent research has demonstrated that DL, particularly convolutional neural networks (CNNs), are an effective strategy for classifying, segmenting, and detecting objects of interest in medical imagesMagnetic resonance imaging (MRI) is a versatile, non-invasive imaging modality offering exceptional resolution and contrast for analyzing soft tissue. MR images have useful therapeutic applications, including diagnostics, due to the varied appearance of organs, tissues, and pathology. Training a DL model requires sufficient training data  and/or their corresponding ground truth. The network uses the training data to adjust its internal parameters (up to many millions), allowing it to map from the input to the required ground truth. The robustness of the model is highly dependent on the inclusion of a large number of relevant samples in the training phase. During deployment, the trained model is applied to unseen samples, leveraging its learned parameters to formulate predictions. Hence, the practical efficacy of the DL framework depends on its successful generalization to unknown datasets.5.The aggregation of multi-center large-scale MRI databases in recent brain research initiatives has provided crucial findings for comprehending the neurobiological aspects underlying brain functions. However, the considerable variations arising from distinct centers, originating from non-biological sources and introducing variability into the neuroimaging data, have hindered the coherent interpretation of reported results. While numerous earlier studies have evaluated various CNNs across diverse MRI datasets, the generalization issue of CNNs on MR images remains. CNNs, as statistical tools, learn the input data’s statistics under the assumption of identical independent distribution (IID). Under this assumption, a trained CNN model is expected to perform consistently on samples with similar or identical distributions. In the context of multi-center MRI datasets, MR images are prone to statistical shifts due to variations in scanner manufacturers and different image acquisition protocols6. Previous investigations have unveiled the limited generalization capability of CNN models across MRI data derived from different manufacturers. Tian et al.7 introduced an MRI harmonization technique that addresses various site effects, including factors such as scanner manufacturer, scanner type, phase encoding direction, and the number of channels per coil. However, the authors reported that the scanner manufacturer factor is the most significant parameter generating the site effects. In another MRI radiomics feature-based study8, the authors observed higher sensitivity in the scanner manufacturer parameter among the three scanner attributes .MRI scanner variations contribute to significant statistical changes, as specific MRI machines provide images with unique properties due to vendor-specific proprietary implementations. The majority of the MRI scans observed in publicly available large datasets come from three renowned manufacturers: General Electric (GE) Healthcare, Philips Medical Systems, and Siemens9, multiple sclerosis (MS)10, brain tumors11 and so on. In a left ventricle (LV) segmentation task, the authors explored the performance variation among three different scanner manufacturers and proposed a manufacturer-adaptation strategy to mitigate scanner bias12. Dadar et al.13 assessed the reliability of gray and white matter volume measurements and the associated variability within multi-site MRI datasets utilizing different scanners. In another investigation14, the authors revealed a variability of 0.15 mm in cortical thickness measurements due to a scanner vendor change (GE/Siemens). However, to the best of our knowledge, no reports have been published demonstrating the impact on the performance of different disease classification tasks, such as patient vs. control normal (CN), due to variations in scanner manufacturers.MRI has been widely used for three decades to diagnose diseases such as acute infarct15 . A popular harmonization method, “ComBat,” was initially introduced to alleviate batch effects in gene expression microarray data and has been proven effective in addressing scanner/site effects in multi-site diffusion tensor imaging (DTI) data16. We aim to evaluate the effectiveness of the standard ComBat and one of the modified ComBat-based harmonization approaches for structural imaging using four large multi-center longitudinal MRI datasets involving three major scanner manufacturers. Existing research shows that ComBat is highly successful in neuroimaging data harmonization, focusing on removing scanner effects from a set of imaging features such as cortical thickness, surface area, and subcortical volumes20. Pomponio et al.21 applied a modified ComBat method to 145 anatomical ROI volumes to eliminate location and scale effects for each ROI. Another study reported better performance by employing radiomic features from lung computed tomography (CT) images with a modified ComBat method22. Nevertheless, the application of a ComBat-based strategy to full-size 3D (NIFTI) images, rather than specific ROIs or extracted features, presents an ongoing challenge. For extensive high-resolution image datasets, memory allocation constraints may impede program execution. Additionally, the ComBat-based strategy requires some demographic data to be available for all samples, such as sex, age, and disease status, which we aim to preserve during harmonization. Importantly, adding a new sample to an existing dataset imposes another concern: the need to rerun the entire harmonization process with the newly added data. Lastly, a recent study23 disclosed that existing statistical harmonization methods like ComBat failed to harmonize cortical thickness from multi-scanner MRI data.Harmonization is a technique used to mitigate variations arising from diverse image acquisition protocols. Multi-center MRI data harmonization aims to remove site-specific bias while preserving intrinsic image propertiesThis work focuses on the non-biological factors of variability in neuroimaging data due to transformations in MRI scanner manufacturers, which pose a barrier to the practical applications of DL algorithms in the medical domain. We highlight how the scanner vendor significantly impacts disease classification performance with multiple DL models. Our investigation delves into the classification of patients with four complex neurodegenerative disorders: Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and the intermediate stage of AD known as mild cognitive impairment (MCI). Our analysis demonstrates a drastic drop in classification accuracy when DL models are tested with data from a different scanner manufacturer. Subsequently, our experiments reveal that employing a ComBat-based harmonization technique could not yield discernible enhancements in classification performance when applied to a multi-center dataset of 3D structural MR images.(ADNI)24 and the Parkinson Progression Marker Initiative (PPMI)25 represent two prominent and extensively studied publicly available datasets in the field of AD and PD detection, respectively. The ADNI protocol received authorization from the committee on human research at each participating center, with written informed consent provided by each participant. Additional information is accessible at adni.loni.usc.edu. The PPMI study was performed following the Declaration of Helsinki and Good Clinical Practice (GCP) policies, in addition to the approval of the local ethics committees of the participating centers. At the time of enrollment, each participant provided written informed consent for using their imaging and clinical data. More details can be found at http://www.ppmi-info.org/. The authors attained approval to use the ADNI and PPMI data in the present study. The Canadian ALS Neuroimaging Consortium (CALSNIC)26 is the only prospective, multi-center and multimodal longitudinal study of ALS using harmonized clinical and imaging protocols across its sites. The CALSNIC study was conducted with the approval of each participating site’s HREB, and informed consent was obtained from the participants. Our study leverages T1-weighted MR images, commonly used for standard structural imaging, acquired from three distinct MRI manufacturers  across the aforementioned datasets. The acquisition orientation of all the MRI data used in our study is sagittal. We employ two versions of ADNI, ADNI1 and ADNI2, consisting of 1638 and 865 MRI scans, respectively. Additionally, our study enlists 528 samples from PPMI and 545 samples from the CALSNIC2 datasets. CALSNIC1 data were excluded from our experiments due to its comparably limited sample size as well as variations in MRI acquisition orientation. An insightful depiction of the demographic composition of our utilized datasets is presented in Table The Health Research Ethics Board (HREB) at the University of Alberta approved the protocol presented in this study. The Alzheimer’s Disease Neuroimaging Initiative FreeSurfer program27 (Command: mri_synthstrip -i input_image -o stripped_image)3. Subsequently, we perform N4 bias field correction using the SimpleITK library’s N4BiasFieldCorrectionImageFilter class to rectify low-frequency intensity non-uniformity in the MRI data28. The Symmetric normalization (SyN) registration technique, implemented through ANTsPy29, is then employed to align each scan with the MNI-152 standard space, utilizing lanczosWindowedSinc interpolation for transformation. Lastly, we apply WhiteStripe intensity normalization using the Python intensity-normalization package30. Upon completing the preprocessing of the original images, their dimensions are transformed to A straightforward, rapid, and commonly employed preprocessing pipeline is implemented to prepare the original 3D T1-weighted brain MRI data for disease classification tasks. The process begins with a standard operation known as skull stripping, aimed at eliminating the unnecessary skull region. This task is achieved using the 31, ShuffleNetV232, and MobileNetV233. Subsequently, we employ two customized models designed explicitly for AD classification. The Residual Network (ResNet), a prominent and influential DL model, was introduced by He et al.31. A pivotal contribution of ResNet is the introduction of “identity shortcut connections,” creating alternate pathways for gradient flow and addressing the vanishing gradient problem in deep CNNs. The fundamental building block of MobileNet33 is depthwise separable convolution, which comprises depthwise convolution and pointwise convolution. Depthwise convolution applies distinct kernels to each input channel, while pointwise convolution employs 32, designed to accommodate mobile device computing limitations, relies on pointwise group convolution and channel shuffling to maintain accuracy while significantly reducing computational load. Qiu et al.1 introduced a 3D customized Fully Convolutional Network (FCN) consisting of six convolutional blocks and then integrated both neuroimaging and clinical data using Multilayer Perceptron (MLP) networks. However, our study only employs their FCN model to handle neuroimaging data. Meanwhile, ADDFormer4, inspired by the vision transformer (ViT) architecture34, combines frequency and spatial domain features in an innovative manner. ADDFormer employs selected coronal 2D slices, and leverages transfer learning by pre-training the network on ImageNet35. Figure To assess the performance of various classification tasks across datasets with distinct scanner vendors, we employ both 2D and 3D DL architectures. Firstly, we utilize three widely recognized and successful networks: ResNet36 and executed on a server equipped with 4 NVIDIA RTX A6000 GPUs. The coding of 3D CNN models is based on publicly available implementations, accessible at https://github.com/xmuyzz/3D-CNN-PyTorch. To enhance training robustness, we employ data augmentation methodologies, including random rotations, flipping, and the mixture of Gaussian noise, to prepare a robust training batch. The optimization process employs the Adam optimizer with an initial learning rate of 0.00005 and a decay rate of Acc metric represents the fraction of accurately identified subjects to the total number of samples in a given dataset, defined as F1-score harmonically combines precision and recall, and is mathematically measured as F1-scoreThe DL frameworks employed in our analysis are implemented using PyTorch31, MobileNetV233, ShuffleNetV232) using MRI data to classify three distinct scanner manufacturers . These well-established CNN-based networks demonstrate exceptional accuracy in classifying the scanner manufacturers. For the ADNI1, ADNI2, and CALSNIC2 datasets, the average classification accuracy exceeds 98%, while the accuracy for the PPMI database ranges between 93% and 96% across all the aforementioned frameworks. The classification outcomes, presented as confusion matrices derived from the ResNet architecture for different datasets, are depicted in Fig. 37 and UMAP (Uniform Manifold Approximation and Projection)38 techniques to visualize the data in a 2D space, using features generated by MRQy39. These visualizations are presented in Fig. https://github.com/rkushol/Effects-of-MRI-scanner-manufacturer.This section presents the results of a series of experiments highlighting the distinctive characteristics of different scanner manufacturers. Initially, we employ three 3D DL-based classification networks , are presented in Table The task of gender classification  from MRI data is comparatively less intricate than the challenge of classifying different neurodegenerative diseases. In this context, we evaluate gender classification across the four previously mentioned datasets to assess performance variations among different scanner manufacturers. The outcomes of gender classification, achieved through distinct 3D CNN-based deep models  and FCN (3D) models outperform other 3D frameworks across various disease classification tasks. Similarly, in the case of 2D networks, the ResNet (2D) and ADDFormer (2D) models achieve better results compared to other 2D DL methods. Table Classifying patients with neurodegenerative diseases such as AD, PD, or ALS from healthy controls using limited MRI data poses significant challenges due to the subtle structural changes present in the images. To enhance the reliability of our findings while maintaining balanced sample sizes across different scanner manufacturers, we leverage longitudinal data. However, a notable exception arises in the CALSNIC2 dataset, where the volume of data from GE and Philips scanners is comparatively smaller compared to that of the Siemens vendor. Moreover, we ensure that our data-splitting strategy avoids data leakage issues. This involves meticulously dividing the data based on individual subjects, preventing mixing the same participant’s images in both training and testing processes, as illustrated in Fig. Firstly, the independent evaluation of AD classification performance across the three manufacturers yields very close accuracy results. The classification accuracy of the top-performing model falls within the range of 85%-88%. Secondly, comparable accuracy is achieved when combining data from all manufacturers, resulting in a sample size approximately three times larger than that of each individual vendor. However, when equalizing the total sample size to that of a single manufacturer , a noticeable decline in performance is observed. Thirdly, among the 3D frameworks, the customized FCN model achieves the highest score, while the ADDFormer model outperforms all others in terms of classification accuracy. On the other hand, a similar conclusion is depicted for the intermediate stage of AD, known as the MCI vs. CN classification task, except that the overall accuracy decreases from all angles.The classification accuracy of ADNI2 slightly surpasses that of ADNI1. Among the three manufacturers, utilizing data from Philips scanners yields slightly better performance compared to data from GE or Siemens. The range of the best model’s classification accuracy falls between 86% and 91%. Upon merging data from all manufacturers, which increases the sample size to approximately three times that of individual vendors, the achieved accuracy remains consistent. However, performance experiences a noticeable decline when the sample size is reduced to that of a single manufacturer, accounting for roughly one-third of the total samples. Once again, among the 3D frameworks of DL models, both the ResNet and the custom-made FCN model achieve better results. In contrast, within the group of 2D methods, the ADDFormer model stands out for achieving the highest classification accuracy.In the PD vs. CN classification task, we initially added a few control samples from the ADNI2 dataset to ensure balanced sample sizes of patients and healthy controls across all three manufacturer groups, thus mitigating severe class imbalance issues. Notably, the FCN and ADDFormer custom-made models also demonstrate strong performance when compared to other fundamental CNN-based methods. The ShuffleNet achieves better outcomes in certain cases within the group of 3D frameworks. The range of the best model’s classification accuracy spans from 72% to 88%. Comparable classification results are observed whether the data originates from GE or Philips scanners. However, the outcomes using data from Siemens scanners are comparatively poor. This discrepancy could be due to sharing a small number of healthy control samples from the ADNI2 dataset, whereas the GE or Philips group shares a large number of control samples from the ADNI2. Likewise, employing a total sample size equivalent to that of an individual manufacturer  leads to a noticeable decline in performance.The classification task involving ALS patients versus healthy controls within the CALSNIC2 database presents an even greater challenge compared to AD classification. All three manufacturers exhibit similar average classification accuracy. However, the performance of data originating from Siemens scanners is notably more reliable due to the inclusion of large samples from multiple centers. The range of the best model’s classification accuracy falls between 78% and 82%. The accuracy remains consistent when the data from all manufacturers are combined. Conversely, the performance experiences a noticeable decline when the sample size from the Siemens manufacturer is reduced to one-third. The number of scans from GE and Philips scanners remains unchanged, as their original sizes are already limited. Among the DL models in both 3D and 2D frameworks, the ADDFormer model once again stands out for its highest classification accuracy.This section examines the consequences of introducing a change in the test set data by employing a different manufacturer. The left panel of Table 21. ComBat-GAM is the only publicly available package that directly handles 3D NIFTI images as input, accessible at https://github.com/rpomponio/neuroHarmonize. This technique successfully estimated age-related volume differences within a large-scale multi-center dataset, segmenting each MR image into 145 ROIs. However, our analysis does not yield promising outcomes when harmonizing entire 3D MRI data, as opposed to limited features extracted from MR images. Supplementary Fig. https://github.com/Jfortin1/ComBatHarmonization. A minor adjustment is made to the original implementation to enable voxel-level harmonization instead of feature-level harmonization, treating each scanner manufacturer as an individual site. From a visual perspective, the outcomes produced by the standard ComBat method closely resemble the original images, with minor changes evident in cortical regions, as depicted in Fig. Initially, we evaluate the outcomes of a modified ComBat-based method known as ComBat-generalized additive model (ComBat-GAM), specifically designed to address site effects in multi-site neuroimaging datasets39 to verify the quality of each MR image. The MRQy tool offers a comprehensive array of quality-related metrics, including peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), coefficient of variation of the foreground patch (CVP) to address shading artifacts, coefficient of joint variation (CJV) to quantify aliasing and inhomogeneity artifacts between foreground and background, and entropy focus criterion (EFC) to detect motion artifacts. The user-friendly interface of MRQy greatly simplifies the process of identifying outliers or inconsistencies within a dataset. Table Alongside manual inspection, we utilize the quality control tool MRQyThe reproducibility of MRI research continues to be challenging, particularly when data is influenced by scanner effects, a type of non-biological variation originating from various image acquisition protocols. After demonstrating significant distinguishable imaging characteristics present in data derived from multiple scanner manufacturers, we explore its consequences for different disease classification tasks using several prominent 2D and 3D DL models.The primary challenge of this study was collecting an adequate number of MRI samples from three major scanner manufacturers . The ADNI has satisfied our initial criterion, which offers the most extensive collection of publicly accessible research data resources, including imaging, clinical, and genomic data. In the ADNI1 and ADNI2 databases, the volume of data originating from Siemens and GE scanners was higher compared to that from Philips scanners. Therefore, we deliberately chose a comparable quantity of data from Siemens and GE vendors to match the data offered by Philips manufacturer. As we utilized longitudinal data, we prioritized including more unique subjects in this random selection scenario. In the context of the PPMI dataset, the data volume was more substantial for the Siemens scanners. Similarly, we limited the data from the Siemens manufacturer in a manner analogous to the sample size of GE and Philips scanners. The number of samples used in training an ML-based methodology, especially in DL models, plays a significant role in achieving satisfactory outcomes. Cohesive and consistent data enhance the performance in analysis, while the presence of heterogeneous characteristics in imaging data presents challenges in obtaining reliable and uniform results.41. Notably, some investigations have further subdivided MCI into progressive (pMCI) and stable (sMCI) subgroups, achieving improved results through such stratification42. The next challenging task is the classification of PD vs. CN. One critical factor that makes this classification task difficult is the heterogeneous nature of the dataset. The PPMI dataset encompasses 21 different centers25, a characteristic evident in Fig. The preprocessing steps applied to our original T1-weighted MR images involve state-of-the-art algorithms and can be easily replicated using open-source tools. After experimenting with a straightforward classification task of differentiating sex  using the original MRI data, we move on to more sophisticated neurodegenerative disease classification tasks. Based on the results obtained from our applied DL models, the most challenging classification task is distinguishing between MCI and CN groups. This finding aligns with prior studies, which have also reported lower accuracy in this specific classification35. Overall, the ADDFormer network demonstrates the best performance in this study, leveraging the power of the ViT architecture by integrating spatial and frequency domain features in a novel manner. In fact, the process of capturing MRI scans initially involves representing data in the frequency domain before converting it to the spatial domain. Hence, effectively utilizing frequency domain features might be the key to achieving enhanced classification performance with MRI data. Furthermore, our study’s 2D models employed significant coronal slices related to disease pathology. The degeneration of nerve cells in brain regions such as the hippocampus, substantia nigra, and corticospinal tract, which are regarded as identifiable regions of interest in the pathogenesis of AD, PD, and ALS, respectively, were captured within the range of selected coronal slices given as input to the ADDFormer and other 2D models used in our study. A recent study similar to the ADDFormer network also demonstrates outstanding performance in the context of ALS classification43.The specialized FCN model consistently outperformed other 3D classification frameworks in most disease classification scenarios. A notable advantage of 3D frameworks lies in their ability to process the entire brain as input, eliminating the need for prior knowledge in selecting specific slices for feature extraction. However, 3D DL methods tend to lack the utilization of pre-trained networks through transfer learning. In contrast, 2D frameworks necessitate the careful selection of relevant 2D slices based on prior knowledge. Additionally, the 2D DL models leverage the transfer learning property by utilizing pre-trained models with a massive 2D imaging dataset like ImageNetThe field of neuroscience research requires robust, efficient, and reliable techniques to address the challenges posed by non-biological sources of data variation due to the increasing demand and necessity for multi-center neuroimaging studies. However, ML-based approaches have demonstrated limitations in producing consistent outcomes when confronted with data collected from diverse centers using distinct MRI scanner models and scanning protocols. Our experimental evaluation highlights the implications of incorporating MRI data from multiple manufacturers for disease classification tasks. Shifting the test domain with data from a different MRI vendor drastically drops the classification accuracy. Developing a novel framework for MRI data harmonization (adjusting scanner variability) becomes essential to effectively leverage multi-center neuroimaging studies. Domain adaptation methods have also emerged as a prominent research avenue in recent years, showing promising results in addressing domain shift and minimizing scanner-related biases. Exploring these solutions could offer valuable insights into refining the harmonization process and improving classification outcomes. Another exciting avenue for future work is analyzing the effects of different scanner models within the same scanner vendor. Last but not least, similar experiments could be conducted with multi-modal neuroimaging data such as FLAIR, functional MRI (fMRI), T2-weighted, and diffusion-weighted images to gain a comprehensive understanding of the effects of MRI scanner manufacturers.Supplementary Information.


Background: Empiric antibiotics are often prescribed in critically ill and preterm neonates at birth until sepsis can be ruled out. Although the current guideline suggests narrow-spectrum antibiotics, an upgrade in antibiotics is common in the neonatal intensive care unit. The impacts of initial broad-spectrum antibiotics on the outcomes of critically ill neonates with respiratory failure requiring mechanical intubation have not been well studied. Methods: A total of 1162 neonates from a tertiary level neonatal intensive care unit (NICU) in Taiwan who were on mechanical ventilation for respiratory distress/failure at birth were enrolled, and neonates receiving ampicillin plus cefotaxime were compared with those receiving ampicillin plus gentamicin. Propensity score-matched analysis was used to investigate the effects of ampicillin plus cefotaxime on the outcomes of critically ill neonates. Results: Ampicillin plus cefotaxime was more frequently prescribed for intubated neonates with lower birth weight, higher severity of illness, and those with a high risk of early-onset sepsis. Only 11.1% of these neonates had blood culture-confirmed early-onset sepsis and/or congenital pneumonia. The use of ampicillin plus cefotaxime did not significantly contribute to improved outcomes among neonates with early-onset sepsis. After propensity score-matched analyses, the critically ill neonates receiving ampicillin plus cefotaxime had significantly worse outcomes than those receiving ampicillin plus gentamicin, including a higher risk of late-onset sepsis caused by multidrug-resistant pathogens , longer duration of hospitalization , and a significantly higher risk of in-hospital mortality . Conclusions: Ampicillin plus cefotaxime should not be routinely prescribed as the empiric antibiotics for critically ill neonates at birth because they were associated with a higher risk of infections caused by multidrug-resistant pathogens and final worse outcomes. Initiation of empiric antibiotics at birth is a common practice in very low birth weight  and critically ill neonates in the neonatal intensive care unit (NICU) . In critThe use of broad-spectrum antibiotics, such as ampicillin plus a third-generation cephalosporin, is known to disturb the gut microbiota and increase the risk of the emergence of antibiotic-resistant pathogens and invasive candidiasis ,9,10,11.During the study period, a total of 1221 neonates with respiratory failure requiring intubation were identified. After excluding neonates with severe congenital anomalies (n = 12), neonates who received ampicillin plus gentamicin or cefotaxime plus metronidazole (n = 14), and neonates who died within the first 48 h of life (n = 33), a total of 1162 neonates were enrolled for analyses. Among them, 670 neonates (57.7%) received ampicillin plus cefotaxime as initial empiric antibiotics, and 492 neonates (42.3%) received ampicillin plus gentamicin as initial empiric antibiotics. The patients’ demographics, perinatal issues, underlying clinical diagnoses of respiratory failure, laboratory results, and ventilator parameters are presented in E. coli  was the most common pathogen followed by Group B Streptococcus  and Listeria monocytogenes . Although Gram-negative bacteria accounted for 51.9% (n = 28) of all EOS episodes, only three EOS episodes were caused by MDR pathogens. A total of six patients died of EOS sepsis and associated infectious complications, and only two of them were treated with initial inappropriate antibiotics. Except for the 129 neonates who received therapeutic antibiotics for ≥7 days due to documented EOS, clinically suspected sepsis, and/or congenital pneumonia, all other cases had empiric antibiotics stopped at 72 h of life in the cohort.In our cohort, only a total of 54 episodes of EOS were documented during the study period. p = 0.014).We found that neonates receiving ampicillin plus cefotaxime had significantly lower birth weight and were more preterm, had more perinatal insults, and had significantly higher severity of illness than the control group, including higher NTISS scores, more abnormal laboratory findings, more requirement of blood transfusions and inotropic agents use, and higher rates of EOS and/or congenital pneumonia. Most of these neonates received initial empiric antibiotics for only 3 days, and only 11.1% (n = 129) had received empiric antibiotics for more than 5 days. Neonates receiving ampicillin plus cefotaxime were significantly more likely to receive empiric antibiotics ≥five days than the control group  than the control group, although the rate of experiencing any nosocomial infections was comparable between these two groups. The in-hospital mortality rate was also significantly higher in neonates receiving ampicillin plus cefotaxime than the controls .For outcome analyses , we founp = 0.027), longer duration of hospitalization , and a significantly higher risk of final in-hospital mortality . The Kaplan–Meier graph also shows a higher risk of final in-hospital mortality in neonates receiving ampicillin plus cefotaxime (p = 0.027 by log-rank test)   when comRecent systemic reviews and meta-analyses found that there is currently inadequate evidence to support any empiric antibiotic regimen being superior to another for neonates with suspected EOS . For empIn our institute, ampicillin plus gentamicin is usually prescribed as the initial empiric antibiotics for common premature or less ill neonates at birth because this regimen can provide adequate coverage of neonatal sepsis . HoweverPrevious studies concluded that neonates using ampicillin plus cefotaxime had a significantly higher mortality rate than those receiving ampicillin plus gentamicin . It is iStreptococcus agalactiae infections. Therefore, we can conclude that ampicillin plus cefotaxime did not contribute significantly to improving the outcomes, even among neonates with documented EOS in our cohort.Based on our data and the literature, culture-confirmed early-onset sepsis occurs in only 1.8–3% of VLBW infants ,24 and oLactobacillus acidophilus and Bifidobacterium bifidum, a capsule (250 mg) once daily for extremely preterm neonates (GA ≤ 28 weeks) in recent years. The incidence rate of late-onset sepsis did not change significantly when compared with our previous study from ten years ago [Recent evidence supports the concept that broad-spectrum antibiotics could destroy gut microbiota, especially those of extremely preterm neonates during the important developmental stage of the early postpartum period ,32. In oears ago . The benears ago .There were some limitations in this study. This was a retrospective cohort study from a single center and the choice of antibiotics depended on the attending physicians. The outcomes of critically ill neonates in the NICU are affected by various characteristics, such as extremely preterm, VLBW, and other chronic comorbidities. Therefore, the conclusion of this study may be affected by many biases and less applicable to other institutes and other countries. Another bias was the non-standard treatment and shift of empiric antibiotics, although only 4.3% (n = 29) of the study group had a modification of empiric antibiotics. The modifications of empiric antibiotics during the critically ill period were affected by the bias of higher severity of illness. Although propensity score matching was performed in this study, it is inevitable that some potential factors or unidentified variables that might affect outcomes were missed. It is not possible to conduct a randomized control trial to investigate the significant impacts of different empirical antibiotics on the outcomes. However, strict antibiotic stewardship programs can be considered for early-onset sepsis and avoidance of broad-spectrum antibiotics should be considered.2 > 60, a pH value > 7.25 and the requirement of fraction of inspired oxygenation (FiO2) > 60 using a noninvasive ventilator, or the presence of hypercarbia (PaCO2 > 60) or persistent apnea despite using a noninvasive ventilator. Neonates with severe congenital anomalies and those who died within 48 h of life were excluded from the analyses. This study was approved by the institutional review board of CGMH, and all methods in this study were performed in accordance with the relevant guidelines and regulations. The need for informed consent was waived because all patient records/information were anonymized and deidentified prior to analysis.We conducted a cohort study and retrospectively reviewed all neonates with respiratory failure requiring mechanical ventilation within the first day of life who were admitted to the NICUs of Chang Gung Memorial Hospital (CGMH) between January 2015 and December 2020. The NICUs of CGMH contained a total of 3 units and a total capacity of 47 beds equipped with ventilators and 60 beds of special care nurseries. The annual number of inpatients in these NICUs is 800, including more than one-fourth of them who were transferred from other hospitals. In our institute, criteria for mechanical ventilation include the following: if we fail to maintain PaOIn our institute, all neonates on mechanical ventilation for respiratory failure or respiratory distress would receive empiric antibiotics with either ampicillin plus gentamicin or ampicillin plus cefotaxime, depending on the decisions of the attending physicians. The empiric antibiotics were administered soon after septic work up and a blood culture from peripheral vein was taken. Only cases with positive blood culture-confirmed early-onset sepsis , clinically suspected sepsis, or congenital pneumonia confirmed by the attending physicians will be administered with therapeutic antibiotics for seven or more than seven days, depending on therapeutic responses. Otherwise, the initial empiric antibiotics were ceased three days later.Patient demographics, their perinatal history, use of empiric antibiotics, EOS, and treatment outcomes of initial respiratory failure or distress were retrospectively reviewed for all neonates who were enrolled in the study. For neonates with documented EOS or congenital pneumonia, therapeutic antibiotics will usually be modified according to the culture results. For clinically suspected sepsis, decisions to modify antibiotics were made on clinical judgement based on therapeutic responses. The severity of illness was routinely evaluated on the first day of life using the neonatal therapeutic intervention scoring system (NTISS) . All patThe definitions of EOS, LOS, nosocomial infections, and congenital pneumonia were based on the updated criteria of the Centers for Disease Control and Prevention (CDC) ,36. All t-test and the paired Wilcoxon rank sum tests. Categorical variables were compared using Chi-square tests or Fisher’s exact tests. All p values were two-tailed, and p values < 0.05 were considered to be statistically significant. All statistical analyses were performed using SPSS .Parametric variables are expressed as mean , and continuous variables with non-parametric distributions are expressed as median . Comparisons between continuous variables of two subgroups were analyzed using paired Student’s We categorized these critically ill neonates with mechanical ventilation into two groups: neonates using ampicillin plus cefotaxime (the study group) and those using ampicillin plus gentamicin (the control group). Neonates initially using ampicillin plus gentamicin but later upgraded to ampicillin plus cefotaxime were categorized as the study group. The primary outcome was final in-hospital mortality. The duration of hospitalization, the incidence rate of LOS, and the occurrence of multidrug-resistant (MDR) pathogens bacteremia, defined based on our previous study , were alPropensity scores using multivariable logistic regression models were calculated within the two groups, which included covariates that may affect the likelihood of patients to receive ampicillin plus either cefotaxime or gentamicin. The outcomes of interest were unbalanced between the two groups before matching. Birth weight and/or gestational age, the NTISS scores soon after birth, which indicated the initial severity of illness, and some risk factors of early-onset sepsis were analyzed in the multivariable logistic regression ,16. MatcIn conclusion, we found that ampicillin plus cefotaxime neither significantly improved the outcomes of neonates with respiratory failure requiring intubation at birth nor provided more coverage for neonatal early-onset sepsis than another combined regimen of ampicillin plus gentamicin. We found that most of the pathogens of EOS were susceptible to ampicillin plus gentamicin, and relatively few patients died of EOS and/or congenital pneumonia. Ampicillin plus cefotaxime should be casually prescribed only in selected critically ill neonates because they were associated with a higher risk of infections caused by MDR pathogens and final worse outcomes. Additionally, future studies that optimize early antibiotic prescription among critically ill and preterm infants without adverse effects are warranted in the future.