Morphometric dataset of Varanus salvator for non-invasive sex identification using machine learning.

Reliable sex identification in Varanus salvator traditionally relied on invasive methods like genetic analysis or dissection, as less invasive techniques such as hemipenes inversion are unreliable. Given the ecological importance of this species and skewed sex ratios in disturbed habitats, a dataset that allows ecologists or zoologists to study the sex determination of the lizard is crucial. We present a new dataset containing morphometric measurements of V. salvator individuals from the skin trade, with sex confirmed by dissection post- measurement. The dataset consists of a mixture of primary and secondary data such as weight, skull size, tail length, condition etc. and can be used in modelling studies for ecological and conservation research to monitor the sex ratio of this species. Validity was demonstrated by training and testing six machine learning models. This dataset has the potential to streamline sex determination, offering a non-invasive alternative to complement existing methods in V. salvator research, mitigating the need for invasive procedures.

Application of computer vision and deep learning models to automatically classify medically important mosquitoes in North Borneo, Malaysia.

Mosquito-borne diseases have emerged in North Borneo in Malaysia due to rapid changes in the forest landscape, and mosquito surveillance is key to understanding disease transmission. However, surveillance programmes involving sampling and taxonomic identification require well-trained personnel, are time-consuming and labour-intensive. In this study, we aim to use a deep leaning model (DL) to develop an application capable of automatically detecting mosquito vectors collected from urban and suburban areas in North Borneo, Malaysia. Specifically, a DL model called MobileNetV2 was developed using a total of 4880 images of Aedes aegypti, Aedes albopictus and Culex quinquefasciatus mosquitoes, which are widely distributed in Malaysia. More importantly, the model was deployed as an application that can be used in the field. The model was fine-tuned with hyperparameters of learning rate 0.0001, 0.0005, 0.001, 0.01 and the performance of the model was tested for accuracy, precision, recall and F1 score. Inference time was also considered during development to assess the feasibility of the model as an app in the real world. The model showed an accuracy of at least 97%, a precision of 96% and a recall of 97% on the test set. When used as an app in the field to detect mosquitoes with the elements of different background environments, the model was able to achieve an accuracy of 76% with an inference time of 47.33 ms. Our result demonstrates the practicality of computer vision and DL in the real world of vector and pest surveillance programmes. In the future, more image data and robust DL architecture can be explored to improve the prediction result.

A protocol and training guidelines for mosquito sampling in remote areas with limited power supply.

Mosquito-borne diseases pose a significant threat in many Southeast Asian countries, particularly through the sylvatic cycle, which has a wildlife reservoir in forests and rural areas. Studying the composition and diversity of vectors and pathogen transmission is especially challenging in forests and rural areas due to their remoteness, limited accessibility, lack of power, and underdeveloped infrastructure. This study is based on the WHO mosquito sampling protocol, modifies technical details to support mosquito collection in difficult-to-access and resource-limited areas. Specifically, we describe the procedure for using rechargeable lithium batteries and solar panels to power the mosquito traps, demonstrate a workflow for processing and storing the mosquitoes in a -20 °C freezer, data management tools including microclimate data, and quality assurance processes to ensure the validity and reliability of the results. A pre- and post-test was utilized to measure participant knowledge levels. Additional research is needed to validate this protocol for monitoring vector-borne diseases in hard-to-reach areas within other countries and settings.

Should Transplant Nephrology Pursue Recognition from the Accreditation Council for Graduate Medical Education (ACGME)?

Kidney transplant is not only the best treatment for patients with advanced kidney disease but it also reduces health care expenditure. The management of transplant patients is complex as they require special care by transplant nephrologists who have expertise in assessing transplant candidates, understand immunology and organ rejection, have familiarity with perioperative complications, and have the ability to manage the long-term effects of chronic immunosuppression. This skill set at the intersection of multiple disciplines necessitates additional training in Transplant Nephrology. Currently, there are more than 250,000 patients with a functioning kidney allograft and over 100,000 waitlisted patients awaiting kidney transplant, with a burgeoning number added to the kidney transplant wait list every year. In 2022, more than 40,000 patients were added to the kidney wait list and more than 25,000 received a kidney transplant. The Advancing American Kidney Health Initiative, passed in 2019, is aiming to double the number of kidney transplants by 2030 creating a need for additional transplant nephrologists to help care for them. Over the past decade, there has been a decline in the Nephrology-as well Transplant Nephrology-workforce due to a multitude of reasons. The American Society of Transplantation Kidney Pancreas Community of Practice created a workgroup to discuss the Transplant Nephrology workforce shortage. In this article, we discuss the scope of the problem and how the Accreditation Council for Graduate Medical Education recognition of Transplant Nephrology Fellowship could at least partly mitigate the Transplant Nephrology work force crisis.

Kidney Transplant Clinicians' Perceptions of Palliative Care for Patients With Failing Allografts in the US: A Mixed Methods Study.

RATIONALE & OBJECTIVE: Kidney transplant patients with failing allografts have a physical and psychological symptom burden as well as high morbidity and mortality. Palliative care is underutilized in this vulnerable population. We described kidney transplant clinicians' perceptions of palliative care to delineate their perceived barriers to and facilitators of providing palliative care to this population. STUDY DESIGN: National explanatory sequential mixed methods study including an online survey and semistructured interviews. SETTING & PARTICIPANTS: Kidney transplant clinicians in the United States surveyed and interviewed from October 2021 to March 2022. ANALYTICAL APPROACH: Descriptive summary of survey responses, thematic analysis of qualitative interviews, and mixed methods integration of data. RESULTS: A total of 149 clinicians completed the survey, and 19 completed the subsequent interviews. Over 90% of respondents agreed that palliative care can be helpful for patients with a failing kidney allograft. However, 46% of respondents disagreed that all patients with failing allografts benefit from palliative care, and two-thirds thought that patients would not want serious illness conversations. More than 90% of clinicians expressed concern that transplant patients and caregivers would feel scared or anxious if offered palliative care. The interviews identified three main themes: (1) transplant clinicians' unique sense of personal and professional responsibility was a barrier to palliative care engagement, (2) clinicians' uncertainty regarding the timing of palliative care collaboration would lead to delayed referral, and (3) clinicians felt challenged by factors related to patients' cultural backgrounds and identities, such as language differences. Many comments reflected an unfamiliarity with the broad scope of palliative care beyond end-of-life care. LIMITATIONS: Potential selection bias. CONCLUSIONS: Our study suggests that multiple barriers related to patients, clinicians, health systems, and health policies may pose challenges to the delivery of palliative care for patients with failing kidney transplants. This study illustrates the urgent need for ongoing efforts to optimize palliative care delivery models dedicated to kidney transplant patients, their families, and the clinicians who serve them. PLAIN-LANGUAGE SUMMARY: Kidney transplant patients experience physical and psychological suffering in the context of their illnesses that may be amenable to palliative care. However, palliative care is often underutilized in this population. In this mixed-methods study, we surveyed 149 clinicians across the United States, and 19 of them completed semistructured interviews. Our study results demonstrate that several patient, clinician, system, and policy factors need to be addressed to improve palliative care delivery to this vulnerable population.

Treatment of Hypercalcemic Hyperparathyroidism After Kidney Transplantation Is Associated With Improved Allograft Survival.

BACKGROUND: Hyperparathyroidism (HPT) and malignancy are the most common causes of hypercalcemia. Among kidney transplant (KT) recipients, hypercalcemia is mostly caused by tertiary HPT. Persistent tertiary HPT after KT is associated with allograft failure. Previous studies on managing tHPT were subjected to survivor treatment selection bias; as such, the impact of tertiary HPT treatment on allograft function remained unclear. We aim to assess the association between hypercalcemic tertiary HPT treatment and kidney allograft survival. MATERIALS AND METHODS: We identified 280 KT recipients (2015-2019) with elevated post-KT adjusted serum calcium and parathyroid hormone (PTH). KT recipients were characterized by treatment: cinacalcet, parathyroidectomy, or no treatment. Time-varying Cox regression with delayed entry at the time of first elevated post-KT calcium was conducted, and death-censored and all-cause allograft failure were compared by treatment groups. RESULTS: Of the 280 recipients with tHPT, 49 underwent PTx, and 98 received cinacalcet. The median time from KT to first elevated calcium was 1 month (IQR: 0-4). The median time from first elevated calcium to receiving cinacalcet and parathyroidectomy was 0(IQR: 0-3) and 13(IQR: 8-23) months, respectively. KT recipients with no treatment had shorter dialysis vintage (P = .017) and lower PTH at KT (P = .002), later onset of hypercalcemia post-KT (P < .001). Treatment with PTx (adjusted hazard ratio (aHR) = 0.18, 95%CI 0.04-0.76, P = .02) or cinacalcet (aHR = 0.14, 95%CI 0.004-0.47, P = .002) was associated with lower risk of death-censored allograft failure. Moreover, receipt of PTx (aHR = 0.28, 95%CI 0.12-0.66, P < .001) or cinacalcet (aHR = 0.38, 95%CI 0.22-0.66, P < .001) was associated with lower risk of all-cause allograft failure. CONCLUSIONS: This study demonstrates that treatment of hypercalcemic tertiary HPT post-KT is associated with improved allograft survival. Although these findings are not specific to hypercalcemia of malignancy, they do demonstrate the negative impact of hypercalcemic tertiary HPT on kidney function. Hypercalcemic HPT should be screened and aggressively treated post-KT.

Medical and Surgical Management of the Failed Pancreas Transplant.

Despite the continued improvements in pancreas transplant outcomes in recent decades, a subset of recipients experience graft failure and can experience substantial morbidity and mortality. Here, we summarize what is known about the failed pancreas allograft and what factors are important for consideration of retransplantation. The current definition of pancreas allograft failure and its challenges for the transplant community are explored. The impacts of a failed pancreas allograft are presented, including patient survival and resultant morbidities. The signs, symptoms, and medical and surgical management of a failed pancreas allograft are described, whereas the options and consequences of immunosuppression withdrawal are reviewed. Medical and surgical factors necessary for successful retransplant candidacy are detailed with emphasis on how well-selected patients may achieve excellent retransplant outcomes. To achieve substantial medical mitigation and even pancreas retransplantation, patients with a failed pancreas allograft warrant special attention to their residual renal, cardiovascular, and pulmonary function. Future studies of the failed pancreas allograft will require improved reporting of graft failure from transplant centers and continued investigation from experienced centers.

Epidemiology, clinical characteristics and outcomes of multisystem inflammatory syndrome in children.

BACKGROUND: We describe the epidemiology, clinical characteristics, and outcomes of multisystem inflammatory syndrome in children (MIS-C) among children from Negeri Sembilan, Malaysia. METHODS: A retrospective, multicentre, observational study was performed among children ≤15 years old who were hospitalized for MIS-C between January 18, 2021 and June 30, 2023. The incidence of MIS-C was estimated using reported SARS-CoV-2 cases and census population data. Descriptive analyses were used to summarize the clinical presentation and outcomes. RESULTS: The study included 53 patients with a median age of 5.7 years (IQR 1.8-8.7 years); 75.5% were males. The overall incidence of MIS-C was approximately 5.9 cases per 1,000,000 person-months. Pediatric intensive care unit (PICU) admission was required for 22 (41.5%) patients. No mortalities were recorded. Children aged 6-12 years were more likely to present with cardiac dysfunction/shock (odds ratio [OR] 5.43, 95% confidence interval [CI] 1.67-17.66), whereas children below 6 years were more likely to present with a Kawasaki disease phenotype (OR 5.50, 95% CI 1.33-22.75). Twenty patients (37.7%) presented with involvement of at least four organ systems, but four patients (7.5%) demonstrated single-organ system involvement. CONCLUSION: An age-based variation in the clinical presentation of MIS-C was demonstrated. Our findings suggest MIS-C could manifest in a spectrum, including single-organ involvement. Despite the high requirement for PICU admission, the prognosis of MIS-C was favorable, with no recorded mortalities.

Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data.

Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need for a model that uses more variables and algorithms that have higher performance. In this study, we use vector indices and meteorological data as predictors to develop the ML models. We trained and validated seven ML algorithms, including an ensemble ML method, and compared their performance using the receiver operating characteristic (ROC) with the area under the curve (AUC), accuracy and F1 score. Our results show that an ensemble ML such as XG Boost, AdaBoost and Random Forest perform better than the logistics regression, Naïve Bayens, decision tree, and support vector machine (SVM), with XGBoost having the highest AUC, accuracy and F1 score. Analysis of the importance of the variables showed that the container index was the least important. By removing this variable, the ML models improved their performance by at least 6% in AUC and F1 score. Our result provides a framework for future studies on the use of predictive models in the development of an early warning system.

The Perspectives of General Nephrologists Toward Transitions of Care and Management of Failing Kidney Transplants.

The management of failing kidney allograft and transition of care to general nephrologists (GN) remain a complex process. The Kidney Pancreas Community of Practice (KPCOP) Failing Allograft Workgroup designed and distributed a survey to GN between May and September 2021. Participants were invited via mail and email invitations. There were 103 respondents with primarily adult nephrology practices, of whom 41% had an academic affiliation. More than 60% reported listing for a second kidney as the most important concern in caring for patients with a failing allograft, followed by immunosuppression management (46%) and risk of mortality (38%), while resistant anemia was considered less of a concern. For the initial approach to immunosuppression reduction, 60% stop antimetabolites first, and 26% defer to the transplant nephrologist. Communicating with transplant centers about immunosuppression cessation was reported to occur always by 60%, and sometimes by 29%, while 12% reported making the decision independently. Nephrologists with academic appointments communicate with transplant providers more than private nephrologists (74% vs. 49%, p = 0.015). There are heterogeneous approaches to the care of patients with a failing allograft. Efforts to strengthen transitions of care and to develop practical practice guidelines are needed to improve the outcomes of this vulnerable population.

Risk factors for disease severity among children with Covid-19: a clinical prediction model.

BACKGROUND: Children account for a significant proportion of COVID-19 hospitalizations, but data on the predictors of disease severity in children are limited. We aimed to identify risk factors associated with moderate/severe COVID-19 and develop a nomogram for predicting children with moderate/severe COVID-19. METHODS: We identified children ≤ 12 years old hospitalized for COVID-19 across five hospitals in Negeri Sembilan, Malaysia, from 1 January 2021 to 31 December 2021 from the state's pediatric COVID-19 case registration system. The primary outcome was the development of moderate/severe COVID-19 during hospitalization. Multivariate logistic regression was performed to identify independent risk factors for moderate/severe COVID-19. A nomogram was constructed to predict moderate/severe disease. The model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS: A total of 1,717 patients were included. After excluding the asymptomatic cases, 1,234 patients (1,023 mild cases and 211 moderate/severe cases) were used to develop the prediction model. Nine independent risk factors were identified, including the presence of at least one comorbidity, shortness of breath, vomiting, diarrhea, rash, seizures, temperature on arrival, chest recessions, and abnormal breath sounds. The nomogram's sensitivity, specificity, accuracy, and AUC for predicting moderate/severe COVID-19 were 58·1%, 80·5%, 76·8%, and 0·86 (95% CI, 0·79 - 0·92) respectively. CONCLUSION: Our nomogram, which incorporated readily available clinical parameters, would be useful to facilitate individualized clinical decisions.

Digitization of natural history collections: A guideline and nationwide capacity building workshop in Malaysia.

Natural history museum collections are the most important sources of information on the present and past biodiversity of our planet. Most of the information is primarily stored in analogue form, and digitization of the collections can provide further open access to the images and specimen data to address the many global challenges. However, many museums do not digitize their collections because of constraints on budgets, human resources, and technologies. To encourage the digitization process, we present a guideline that offers low-cost and technical knowledge solutions yet balances the quality of the work and outcomes. The guideline describes three phases of digitization, namely preproduction, production, and postproduction. The preproduction phase includes human resource planning and selecting the highest priority collections for digitization. In the preproduction phase, a worksheet is provided for the digitizer to document the metadata, as well as a list of equipment needed to set up a digitizer station to image the specimens and associated labels. In the production phase, we place special emphasis on the light and color calibrations, as well as the guidelines for ISO/shutter speed/aperture to ensure a satisfactory quality of the digitized output. Once the specimen and labels have been imaged in the production phase, we demonstrate an end-to-end pipeline that uses optical character recognition (OCR) to transfer the physical text on the labels into a digital form and document it in a worksheet cell. A nationwide capacity workshop is then conducted to impart the guideline, and pre- and postcourse surveys were conducted to assess the confidence and skills acquired by the participants. This paper also discusses the challenges and future work that need to be taken forward for proper digital biodiversity data management.

Resolution of Secondary Hyperparathyroidism After Kidney Transplantation and the Effect on Graft Survival.

OBJECTIVE: Hyperparathyroidism (HPT) is nearly universal in patients with end-stage kidney disease. Kidney transplantation (KT) reverses HPT in many patients, but most studies have only focused on following calcium and not parathyroid hormone (PTH) levels. We sought to study the prevalence of persistent HPT post-KT at our center and its effect on graft survival. METHODS: Patients who underwent KT from January 2015 to August 2021 were included and characterized by post-KT HPT status at the most recent follow-up: resolved (achieving normal PTH post-KT) versus persistent HPT. Those with persistent HPT were further stratified by the occurrence of hypercalcemia (normocalcemic versus hypercalcemic HPT). Patient demographics, donor kidney quality, PTH and calcium levels, and allograft function were compared between groups. Multivariable logistic regression and Cox regression with propensity score matching were conducted. RESULTS: Of 1554 patients, only 390 (25.1%) patients had resolution of renal HPT post-KT with a mean (±SD) follow-up length of 40±23 months. The median (IQR) length of HPT resolution was 5 (0-16) months. Of the remaining 1164 patients with persistent HPT post-KT, 806 (69.2%) patients had high PTH and normal calcium levels, while 358 (30.8%) patients had high calcium and high PTH levels. Patients with persistent HPT had higher parathyroid hormone (PTH) at the time of KT [403 (243-659) versus 277 (163-454) pg/mL, P <0.001] and were more likely to have received cinacalcet treatment before KT (34.9% vs. 12.3%, P <0.001). Only 6.3% of patients with persistent HPT received parathyroidectomy. Multivariable logistic regression showed race, cinacalcet use pre-KT, dialysis before KT, receiving an organ from a deceased donor, high PTH, and calcium levels at KT were associated with persistent HPT post-KT. After adjusting for patient demographics and donor kidney quality by propensity score matching, persistent HPT (HR 2.5, 95% CI 1.1-5.7, P =0.033) was associated with a higher risk of allograft failure. Sub-analysis showed that both hypercalcemic HPT (HR 2.6, 95% CI 1.1-6.5, P =0.045) and normocalcemic HPT (HR 2.5, 95% CI 1.3-5.5, P =0.021) were associated with increased risk of allograft failure when compared with patients with resolved HPT. CONCLUSION: Persistent HPT is common (75%) after KT and is associated with a higher risk of allograft failure. PTH levels should be closely monitored after kidney transplantation so that patients with persistent HPT can be treated appropriately.

B cell and antibody targeting of transplant alloantigen epitopes is supported by both self and allo-recognition.

Donor-specific antibody (DSA) responses against human leukocyte antigen (HLA) proteins mismatched between kidney transplant donors and recipients cause allograft loss. Using single-cell, molecular, structural, and proteomic techniques, we profiled the HLA-specific (alloreactive) B cell response in kidney and blood of a transplant recipient with antibody-mediated rejection (AMR). We identified 14 distinct alloreactive B cell lineages, which spanned the rejected organ and blood and expressed high-affinity anti-donor HLA-specific B cell receptors, many of which were clonally linked to circulating DSA. The alloreactive B cell response was focused on exposed, solvent-accessible mismatched HLA residues, while also demonstrating extensive contacts with self-HLA residues. Consistent with structural evidence of self-recognition, measurable self-reactivity by donor-specific B cells was common and positively correlated with anti-donor affinity maturation. Thus, allo- and self-reactive signatures appeared to converge, suggesting that during AMR, the recognition of non-self and breaches of tolerance conspire to produce a pathogenic donor-specific adaptive response.

A protocol for developing a classification system of mosquitoes using transfer learning.

Mosquito identification and classification are the most important steps in a surveillance program of mosquito-borne diseases. With conventional approach of data collection, the process of sorting and classification are laborious and time-consuming. The advancement of computer vision with transfer learning provides excellent alternative to the challenge. Transfer learning is a type of machine learning that is viable and durable in image classification with limited training images. This protocol aims to develop step-by-step procedure in developing a classification system with transfer learning algorithm for mosquito, we demonstrate the protocol to classify two species of Aedes mosquito - Aedes aegypti L. and Aedes albopitus L, but user can adopt the protocol for higher number of species classification. We demonstrated the way of start from the scratch, fine-tuning two pre-trained model performance by using different combination of hyperparameters - batch size and learning rate, and explain the terminology in the Appendix. This protocol target on the domain expert such as entomologist and public health practices to develop their own model to solve the task of mosquito/insect classification.

Making the Cut: Parathyroidectomy Before or After Kidney Transplantation?

BACKGROUND: Hyperparathyroidism is common in patients with end-stage kidney disease and may persist after kidney transplantation (KT). Parathyroidectomy (PTx) is curative, but whether PTx should be performed before or after KT remains controversial. There is concern that PTx can adversely affect renal allograft function if performed post-KT and result in persistent hypocalcemia. This study evaluated outcomes and postoperative complications of PTx before and after KT at our institution. METHODS: We performed a retrospective review of patients at our center (1/2012-2/2019) who had PTx either pre-KT or post-KT. Data on patient demographics, surgical outcomes, and postoperative complications of PTx were collected. RESULTS: Ninety-eight patients were included in this study, with 23 patients undergoing PTx before KT and 75 after KT. The length of follow-up after KT was 67.7 ± 25.5 months. In post-KT PTx patients, 30-day allograft function was unchanged after PTx. Calcium oxalate and phosphate crystals were less common on allograft biopsies in pre-KT PTx patients (10.0% vs. 34.8%, p = 0.038). Patients in the pre-KT group required more calcium supplementation after PTx than the post-KT group (p < 0.001). At one-year post-PTx, 17 (19.1%) patients required > 1000 mg elemental calcium per day and 7 (7.9%) patients required > 2000 mg/day. There was no difference in surgical success or postoperative complications between the two groups. CONCLUSIONS: Parathyroidectomy before or after kidney transplantation does not adversely affect allograft function. The incidence of persistent hypocalcemia was low. Parathyroidectomy is safe and effective either before or after kidney transplantation.

Next generation insect taxonomic classification by comparing different deep learning algorithms.

Insect taxonomy lies at the heart of many aspects of ecology, and identification tasks are challenging due to the enormous inter- and intraspecies variation of insects. Conventional methods used to study insect taxonomy are often tedious, time-consuming, labor intensive, and expensive, and recently, computer vision with deep learning algorithms has offered an alternative way to identify and classify insect images into their taxonomic levels. We designed the classification task according to the taxonomic ranks of insects-order, family, and genus-and compared the generalization of four state-of-the-art deep convolutional neural network (DCNN) architectures. The results show that different taxonomic ranks require different deep learning (DL) algorithms to generate high-performance models, which indicates that the design of an automated systematic classification pipeline requires the integration of different algorithms. The InceptionV3 model has advantages over other models due to its high performance in distinguishing insect order and family, which is having F1-score of 0.75 and 0.79, respectively. Referring to the performance per class, Hemiptera (order), Rhiniidae (family), and Lucilia (genus) had the lowest performance, and we discuss the possible rationale and suggest future works to improve the generalization of a DL model for taxonomic rank classification.

An annotated image dataset of medically and forensically important flies for deep learning model training.

Conventional methods to study insect taxonomy especially forensic and medical dipterous flies are often tedious, time-consuming, labor-intensive, and expensive. An automated recognition system with image processing and computer vision provides an excellent solution to assist the process of insect identification. However, to the best of our knowledge, an image dataset that describes these dipterous flies is not available. Therefore, this paper introduces a new image dataset that is suitable for training and evaluation of a recognition system involved in identifying the forensic and medical importance of dipterous flies. The dataset consists of a total of 2876 images, in the input dimension (224 × 224 pixels) or as an embedded image model (96 × 96 pixels) for microcontrollers. There are three families (Calliphoridae, Sarcophagidae, Rhiniidae) and five genera (Chrysomya, Lucilia, Sarcophaga, Rhiniinae, Stomorhina), and each class of genus contained five different variants (same species) of fly to cover the variation of a species.

COVID-19 Vaccination and Remdesivir are Associated With Protection From New or Increased Levels of Donor-Specific Antibodies Among Kidney Transplant Recipients Hospitalized With COVID-19.

Alloimmune responses in kidney transplant (KT) patients previously hospitalized with COVID-19 are understudied. We analyzed a cohort of 112 kidney transplant recipients who were hospitalized following a positive SARS-CoV-2 test result during the first 20 months of the COVID-19 pandemic. We found a cumulative incidence of 17% for the development of new donor-specific antibodies (DSA) or increased levels of pre-existing DSA in hospitalized SARS-CoV-2-infected KT patients. This risk extended 8 months post-infection. These changes in DSA status were associated with late allograft dysfunction. Risk factors for new or increased DSA responses in this KT patient cohort included the presence of circulating DSA pre-COVID-19 diagnosis and time post-transplantation. COVID-19 vaccination prior to infection and remdesivir administration during infection were each associated with decreased likelihood of developing a new or increased DSA response. These data show that new or enhanced DSA responses frequently occur among KT patients requiring admission with COVID-19 and suggest that surveillance, vaccination, and antiviral therapies may be important tools to prevent alloimmunity in these individuals.