Factors associated with resistance to SARS-CoV-2 infection discovered using large-scale medical record data and machine learning.

There have been over 621 million cases of COVID-19 worldwide with over 6.5 million deaths. Despite the high secondary attack rate of COVID-19 in shared households, some exposed individuals do not contract the virus. In addition, little is known about whether the occurrence of COVID-19 resistance differs among people by health characteristics as stored in the electronic health records (EHR). In this retrospective analysis, we develop a statistical model to predict COVID-19 resistance in 8,536 individuals with prior COVID-19 exposure using demographics, diagnostic codes, outpatient medication orders, and count of Elixhauser comorbidities in EHR data from the COVID-19 Precision Medicine Platform Registry. Cluster analyses identified 5 patterns of diagnostic codes that distinguished resistant from non-resistant patients in our study population. In addition, our models showed modest performance in predicting COVID-19 resistance (best performing model AUROC = 0.61). Monte Carlo simulations conducted indicated that the AUROC results are statistically significant (p < 0.001) for the testing set. We hope to validate the features found to be associated with resistance/non-resistance through more advanced association studies.

Acellular nerve grafts with bone marrow mesenchymal stem cells/Schwann cells for sciatic nerve regeneration in rats: a meta-analysis.

INTRODUCTION: The aim of this meta-analysis was to evaluate the effects of acellular nerve grafts (ANGs) with bone marrow mesenchymal stem cells (BMSCs) or Schwann cells (SCs) on the treatment of sciatic nerve defect in rats. EVIDENCE ACQUISITION: Electronic databases were accessed to identify eligible targets. ANGs data were extracted for meta-analysis using Review Manager 5.3. EVIDENCE SYNTHESIS: The rats subjected to ANGs+BMSCs or ANGs+SCs are characterized by different sciatic nerve function index, nerve conduction, latency, amplitude, myelin sheath thickness, myelinated nerve fibers and gastrocnemius wet weight. accompanied with evidently superior recovery of limb function. These differences are of statistical significance (P<0.05) when compared to that of control group with ANGs only. CONCLUSIONS: ANGs with BMSCs or SCs can promote nerve regeneration and functional recovery in peripheral nerve defects.

Pulsed Vacuum Drying of Persimmon Slices: Drying Kinetics, Physicochemical Properties, Microstructure and Antioxidant Capacity.

In order to explore an alternative drying method to enhance the drying process and quality of persimmon slices, pulsed vacuum drying (PVD) was employed and the effects of different drying temperatures (60, 65, 70, and 75 °C) on drying kinetics, color, rehydration ratio (RR), microstructure, bioactive compounds, and the antioxidant capacity of sliced persimmons were investigated in the current work. Results showed that the rehydration ratio (RR) of the samples under PVD was significantly higher than that of the traditional hot air-dried ones. Compared to the fresh samples, the dried persimmon slices indicated a decrease in the bioactive compounds and antioxidant capacity. The total phenolic content (TPC) of PVD samples at 70 °C was 87.96% higher than that of the hot air-dried persimmon slices at 65 °C. Interestingly, at 70 °C, the soluble tannin content and TPC of the PVD samples reached the maximum values of 6.09 and 6.97 mg GAE/g, respectively. The findings in the current work indicate that PVD is a promising drying method for persimmon slices as it not only enhances the drying process but also the quality attributes.

Ileocolic Thrombophlebitis and Lymphadenitis Mimicking Acute Appendicitis as a Late Manifestation in a COVID-19 Patient: A Case Report.

Coronavirus disease 2019 (COVID-19) is an infectious viral disease, manifesting primarily as a lung infection with fever and respiratory symptoms. However, it also has a wide range of gastrointestinal symptoms, including nausea, vomiting, abdominal pain, and diarrhea. Right lower quadrant (RLQ) abdominal pain is a common complaint for patients seeking care at emergency departments. In addition to appendicitis, the other possible causes include diverticular disease, epiploic appendagitis, Crohn's disease, or mesenteric lymphadenitis, among others. Mesenteric ischemia is an uncommon, but crucial cause of abdominal pain, necessitating early diagnosis and treatment. Herein, we report a 47-year-old man who presented to our emergency department complaining of RLQ abdominal pain following recovery from COVID-19. CT was performed due to concern for acute appendicitis. However, mesenteric thrombophlebitis and lymphadenitis in the ileocolic branch were noted on CT. His abdominal pain improved after receiving anticoagulation therapy. This case describes an uncommon etiology of RLQ abdominal pain that should be considered as a late complication of COVID-19.

Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic.

Clinical evidence suggests that some patients diagnosed with coronavirus disease 2019 (COVID-19) experience a variety of complications associated with significant morbidity, especially in severe cases during the initial spread of the pandemic. To support early interventions, we propose a machine learning system that predicts the risk of developing multiple complications. We processed data collected from 3,352 patient encounters admitted to 18 facilities between April 1 and April 30, 2020, in Abu Dhabi (AD), United Arab Emirates. Using data collected during the first 24 h of admission, we trained machine learning models to predict the risk of developing any of three complications after 24 h of admission. The complications include Secondary Bacterial Infection (SBI), Acute Kidney Injury (AKI), and Acute Respiratory Distress Syndrome (ARDS). The hospitals were grouped based on geographical proximity to assess the proposed system's learning generalizability, AD Middle region and AD Western & Eastern regions, A and B, respectively. The overall system includes a data filtering criterion, hyperparameter tuning, and model selection. In test set A, consisting of 587 patient encounters (mean age: 45.5), the system achieved a good area under the receiver operating curve (AUROC) for the prediction of SBI (0.902 AUROC), AKI (0.906 AUROC), and ARDS (0.854 AUROC). Similarly, in test set B, consisting of 225 patient encounters (mean age: 42.7), the system performed well for the prediction of SBI (0.859 AUROC), AKI (0.891 AUROC), and ARDS (0.827 AUROC). The performance results and feature importance analysis highlight the system's generalizability and interpretability. The findings illustrate how machine learning models can achieve a strong performance even when using a limited set of routine input variables. Since our proposed system is data-driven, we believe it can be easily repurposed for different outcomes considering the changes in COVID-19 variants over time.

Protein coding content of the UL)b' region of wild-type rhesus cytomegalovirus.

A recent comparison of two rhesus cytomegalovirus (RhCMV) genomes revealed that the region at the right end of the U(L) genome component (U(L)b') undergoes genetic alterations similar to those observed in serially passaged human cytomegalovirus (HCMV). To determine the coding content of authentic wild-type RhCMV in this region, the U(L)b' sequence was amplified from virus obtained from naturally infected rhesus macaques without passage in vitro. A total of 24 open reading frames (ORFs) potentially encoding >99 amino acid residues were identified, 10 of which are related to HCMV ORFs and 15 to previously listed RhCMV ORFs. In addition, the analysis revealed a cluster of three novel alpha chemokine-like ORFs, bringing the number of predicted alpha chemokine genes in this region to six. Three of these six genes exhibit a high level of sequence diversity, as has been observed for the HCMV alpha chemokine gene UL146.