Polyhydroxybutyrate Plastics Show Rapid Disintegration and More Straightforward Biogeochemical Impacts than Polyethylene under Marine Biofragmentation.

Although massive studies have investigated the spatiotemporally occurring marine plastisphere, a new microbial ecosystem colonizing the surfaces of plastics, the resulting biofragmentation process and impacts of plastics on biogeochemical cycles remain largely unknown. Here, we leverage synchrotron-based Fourier transform infrared spectromicroscopy (FTIR mapping) and metagenomic sequencing to explore independent marine microcosms amended with petroleum-based polyethylene (PE) and biobased polyhydroxybutyrate (PHB) plastic films. FTIR mapping results demonstrate unequal fragmentation scenarios by which the PE plastic rarely releases oxidized fragments while PHB disintegrates quickly, gradually forming fragments composed of extracellular polymeric substances resembling plastic films. Metagenomic analysis shows the critical role of hydrocarbonoclastic lineages in the biodegradation of the two plastics by the fatty acid degradation pathway, where the PE plastics host different microbial trajectories between the plastisphere (dominated by Alcanivorax) and surrounding seawater. In contrast, the PHB addition demonstrates decreased microbial richness and diversity, consistent community composition (dominated by Phaeobacter and Marinobacter), and apparently stimulated sulfur cycle and denitrification pathways in both the plastisphere and surrounding seawater. Our study gives scientific evidence on the marine biotic processes distinguishing petroleum- and biobased plastics, highlighting marine PHB input exerting straightforward impacts on the water phase and deserving critical management practices.

Effect of Oat Beta-Glucan on Physicochemical Properties and Digestibility of Fava Bean Starch.

The current research examined the impact of different concentrations of oat beta-glucan (OG) on the in vitro digestibility of fava bean starch (FS). Our pasting analysis demonstrated that OG effectively decreased the viscosity and regrowth of FS, suppressing its in situ regrowth while enhancing the in vitro pasting temperature. Moreover, OG markedly diminished amylose leaching and minimized the particle size of the pasted starch. Rheological and textural evaluations demonstrated that OG markedly diminished the viscoelasticity of the starch and softened the gel strength of the composite system. Structural analysis revealed that hydrogen bonding is the primary interaction in the FS-OG system, indicating that OG interacts with amylose through hydrogen bonding, thereby delaying starch pasting and enhancing the gelatinization characteristics of FS gels. Notably, the incorporation of OG resulted in a reduction in the levels of rapidly digestible starch (RDS) and slowly digestible starch (SDS) in FS, accompanied by a notable increase in resistant starch (RS) content, from 21.30% to 31.82%. This study offers crucial insights for the application of OG in starch-based functional foods.

Causal prior-embedded physics-informed neural networks and a case study on metformin transport in porous media.

This study introduces a novel approach to transport modelling by integrating experimentally derived causal priors into neural networks. We illustrate this paradigm using a case study of metformin, a ubiquitous pharmaceutical emerging pollutant, and its transport behaviour in sandy media. Specifically, data from metformin's sandy column transport experiment was used to estimate unobservable parameters through a physics-based model Hydrus-1D, followed by a data augmentation to produce a more comprehensive dataset. A causal graph incorporating key variables was constructed, aiding in identifying impactful variables and estimating their causal dynamics or "causal prior." The causal priors extracted from the augmented dataset included underexplored system parameters such as the type-1 sorption fraction F, first-order reaction rate coefficient α, and transport system scale. Their moderate impact on the transport process has been quantitatively evaluated (normalized causal effect 0.0423, -0.1447 and -0.0351, respectively) with adequate confounders considered for the first time. The prior was later embedded into multilayer neural networks via two methods: causal weight initialization and causal prior regularization. Based on the results from AutoML hyperparameter tuning experiments, using two embedding methods simultaneously emerged as a more advantageous practice since our proposed causal weight initialization technique can enhance model stability, particularly when used in conjunction with causal prior regularization. amongst those experiments utilizing both techniques, the R-squared values peaked at 0.881. This study demonstrates a balanced approach between expert knowledge and data-driven methods, providing enhanced interpretability in black-box models such as neural networks for environmental modelling.

Versatile ionic liquid gels formed by dynamic covalent bonding and microphase separated structures.

It is challenging for ionic liquid gels to achieve the combination of rapid self-healing with high toughness. Here, ionic liquid gels (DI-PR) were prepared from readily available materials. A dynamic covalently bonded oxime-carbamate was prepared from polycaprolactone diol, isophorone diisocyanate and dimethylethyleneglyoxime, followed by addition of the "rigid-flexible" cross-linking agent rutin to chemically cross-link the polymer chains and afford the ionic liquid gels, DI-PR. The tensile strength, elongation at break and toughness of the DI-PR gels were as high as 16.5 MPa, 1132.6%, and 52.6 MJ m-3, respectively. The toughness is similar to that of natural silkworm silk (70 MJ m-3) and wool (60 MJ m-3). After stretching, the DI-PR can rebound within 1 s, their room temperature self-healing rate is as high as 92%, they remain functional over the temperature range -50 °C to 140 °C and the interface with a steel plate has an adhesion toughness of >2000 J m-2. These properties mean that the DI-PR gels are particularly suitable for use as anticorrosion coatings for submarine and underground gas and oil pipelines. The use of rutin, which combines rigid quercetin-based structural units with flexible glycoside-based structural units, as a cross-linking agent, provides a new method for improving the toughness of soft materials through its synergistic interaction with hard and soft chain fragments of polyurethanes.

Claims data analysis of provider-to-provider tele-mentoring program impact on opioid prescribing in Missouri.

OBJECTIVE: The objective of this study was to assess opioid prescribing patterns of primary care providers (PCPs) participating in a virtual tele-mentoring program for patients with chronic pain as compared to nonparticipants. DESIGN: We utilized Missouri Medicaid claims from 2013 to 2021 to compare opioid prescription dosages and daily supply of opioids prescribed by PCPs. Participants and nonparticipants were matched using propensity score matching. SETTING: Missouri Medicaid data were received through partnership with the Center for Health Policy's MO HealthNet Data Project, the state's leading provider of Medicaid data. PARTICIPANTS: Missouri-based prescribers. INTERVENTION: Show-Me Project Extension for Community Healthcare Outcomes (ECHO), an evidence-based provider-to-provider telehealth intervention that connects PCPs with a team of specialists. MAIN OUTCOME MEASURES: We compared the rate of prescription opioid >50 morphine milligram equivalents (MMEs), mean MMEs/day, and mean number of daily supply to understand the impact of the ECHO model on providers' opioid prescribing. RESULTS: Patients treated by ECHO providers have 33 percent lower odds of being prescribed opioid dose >50 MME/day (p < 0.001) compared to non-ECHO providers. There is also a 14 percent reduction in the average opioid dose prescribed to patients of ECHO providers (p < 0.001). We observed a 3 percent (p < 0.001) reduction in average daily supply of opioids among patients of ECHO providers compared to the comparison group. CONCLUSIONS: Pain Management ECHO supports PCPs with needed education and skills to provide specialty care in the management of pain conditions and safe prescribing of opioid medications.

Possible mechanisms of auricular acupoint stimulation in the treatment of migraine by activating auricular vagus nerve. / 从耳迷走神经探讨耳穴治疗偏头痛的可能机制.

Under the guidance of traditional Chinese medicine theory, the clinical research of auricular acupoint stimulation in the treatment of migraine has gained a lot, and the curative efficacy is definite, but its mechanism remains unclear. In the present paper, we discussed the efficacy of auricular acupoint stimulation including "transcutaneous auricular vagus nerve stimulation" (taVNS) in the treatment of migraine in recent years. Through bibliometric analysis, we screened out top 10 auricular acupoints (Shenmenï¼»TF4ï¼½, Pizhixiaï¼»AT4ï¼½, Jiaoganï¼»AH6aï¼½, Ganï¼»CO12ï¼½, Yidanï¼»CO11ï¼½, Neifenmiï¼»CO18ï¼½, Shenï¼»CO10ï¼½, Nieï¼»AT2ï¼½, Zhenï¼»AT3ï¼½ and Eï¼»AT1ï¼½) which were the most frequently used for migraine. Majority of these auricular acupoints just distributed in the region innervated by auricular vagus nerve. Thus, we thought that the analgesic effect of needling these auricular acupoints for migraine was produced by triggering the auricular vagus nerve, and concluded that the central mechanism underlying induction of analgesic effect by activating auricular vagus nerve may be achieved by activating the descending pain regulation pathway of the locus coeruleus nucleus and dorsal raphe nucleus. In addition, taVNS-induced 1) regulation of the activities of brain's default network and pain matrix, 2) activation of the cortical descending pain regulation pathway, and 3) inhibition of the neuroinflammatory response may also contribute to its ameliorating effect of migraine. This paper may provide ideas for the future research on the mechanism of auricular acupoint treatment of migraine.

Glutamatergic neurons in the paraventricular nucleus of the hypothalamus participate in the regulation of visceral pain induced by pancreatic cancer in mice.

Background: Visceral pain induced by pancreatic cancer seriously affects patients' quality of life, and there is no effective treatment, because the mechanism of its neural circuit is unknown. Therefore, the aim of this study is to explore the main neural circuit mechanism regulating visceral pain induced by pancreatic cancer in mice. Methods: The mouse model of pancreatic cancer visceral pain was established on C57BL/6N mice by pancreatic injection of mPAKPC-luc cells. Abdominal mechanical hyperalgesia and hunch score were performed to assess visceral pain; the pseudorabies virus (PRV) was used to identify the brain regions innervating the pancreas; the c-fos co-labeling method was used to ascertain the types of activated neurons; in vitro electrophysiological patch-clamp technique was used to record the electrophysiological activity of specific neurons; the calcium imaging technique was used to determine the calcium activity of specific neurons; specific neuron destruction and chemogenetics methods were used to explore whether specific neurons were involved in visceral pain induced by pancreatic cancer. Results: The PRV injected into the pancreas was detected in the paraventricular nucleus of the hypothalamus (PVN). Immunofluorescence staining showed that the majority of c-fos were co-labeled with glutamatergic neurons in the PVN. In vitro electrophysiological results showed that the firing frequency of glutamatergic neurons in the PVN was increased. The calcium imaging results showed that the calcium activity of glutamatergic neurons in the PVN was enhanced. Both specific destruction of glutamatergic neurons and chemogenetics inhibition of glutamatergic neurons in the PVN alleviated visceral pain induced by pancreatic cancer. Conclusions: Glutamatergic neurons in the PVN participate in the regulation of visceral pain induced by pancreatic cancer in mice, providing new insights for the discovery of effective targets for the treatment of pancreatic cancer visceral pain.

A multi-criteria simulation-optimization coupling approach for effective emergency response in marine oil spill accidents.

Effective marine oil spill responses are vital to reduce environmental, societal, and economic damage. This study developed a Multi-Criteria Emergency Response System (MC-ERS) to comprehensively evaluate response efficiency, operational costs, and environmental losses. The proposed system integrates dynamic multiphase simulation of oil weathering and oil cleanup processes and further provides effective planning for multi-stage resource allocation through system optimization. The developed weight-sum model improved the performance of response operations by reducing the complexity of multi-criteria decision-making. Particle Swarm Optimization (PSO) was chosen as the foundational optimization algorithm due to its efficiency in rapid convergence and suitability for complex problems. From extensive comparisons of PSO variants across benchmark functions and inertia strategies, the C-PSO algorithm was developed, demonstrating enhanced optimization performance for MC-ERS. The developed modelling system performance was demonstrated and evaluated through a representative case study. The optimization plan coordinated resource allocation from onshore warehouses to harbors and spill sites, balancing oil recovery efficiency, costs, and ecological losses. Optimized results indicate an oil recovery of up to 76.50% in five days. Additionally, the system cuts costs by 3.45% and environmental losses by 15.75%. The findings enhance the efficiency of marine oil spill emergency response and provide support for such incidents.

Fitness-for-use of Retrospective Multicenter Electronic Health Records to Conduct Outcome Analysis for Pediatric Ulcerative Colitis.

The use of electronic health records has garnered interest as an approach for conducting innovative outcome research and producing real-world evidence at a reduced cost compared to traditional clinical trials. The study aimed to evaluate the utility of deidentified EHR data from a multicenter research network to identify characteristics associated with treatment escalation (TE) in newly diagnosed pediatric ulcerative colitis patients. EHR data (01/2010-12/2021) from 13 Midwest healthcare systems (Greater Plains Collaborative) were collected for pediatric ulcerative colitis patients. We identified standard treatments, excluded missing initial therapy data, and analyzed the TE and time-to-TE outcomes. The clinical and laboratory characteristics at baseline were extracted. Logistic and Cox models were used, and the missing risk factors were imputed. Machine-learning Bayesian additive regression trees were also utilized to create partial dependence plots for assessing the associations between risk factors and clinical outcomes. A total of 502 eligible pediatric patients (aged 4-17 years) who initiated standard treatment were identified. Among them, 205 out of 502 (41%) experienced TE, with a median (P25, P75) duration of 63 (9, 237) days after the initial treatment. Additionally, 20 out of 509 (4%) patients underwent colectomy (COL) with a median (P25, P75) duration of 80 (3, 205) days. Both multivariable logistic regression and Cox proportional hazards regression demonstrated moderate discriminative power in predicting TE and time-to-TE, respectively. Common positive predictors for both TE and time-to-TE included a high monocyte proportion and elevated platelet counts. Conversely, BMI z-score, albumin, hemoglobin levels, and lymphocyte proportion were negatively associated with both TE and time-to-TE. This study demonstrates that multicenter EHR data can be used to identify a trial-comparable study sample of potentially larger size and to identify clinically meaningful endpoints for conducting outcome analysis and generating real-world evidence.

A comparison of Bayesian and frequentist approaches to incorporating clinical and biological information for the prediction of response to standardized pediatric colitis therapy.

BACKGROUND: The prospective cohort study PROTECT is the largest study in pediatric ulcerative colitis (UC) with standardized treatments, providing valuable data for predicting clinical outcomes. PROTECT and previous studies have identified characteristics associated with clinical outcomes. In this study, we aimed to compare predictive modeling between Bayesian analysis including machine learning and frequentist analysis. METHODS: The key outcomes for this analysis were week 4, 12 and 52 corticosteroid (CS)-free remission following standardized treatment from diagnosis. We developed predictive modeling with multivariable Bayesian logistic regression (BLR), Bayesian additive regression trees (BART) and frequentist logistic regression (FLR). The effect estimate of each risk factor was estimated and compared between the BLR and FLR models. The predictive performance of the models was assessed including area under curve (AUC) of the receiver operating characteristic (ROC) curve. Ten-fold cross-validation was performed for internal validation of the models. The estimation contained 95% credible (or confidence) interval (CI). RESULTS: The statistically significant associations between the risk factors and early or late outcomes were consistent between all BLR and FLR models. The model performance was similar while BLR and BART models had narrower credible intervals of AUCs. To predict week 4 CS-free remission, the BLR model had AUC of 0.69 (95% CI 0.67-0.70), the BART model had AUC of 0.70 (0.67-0.72), and the FLR had AUC of 0.70 (0.65-0.76). To predict week 12 CS-free remission, the BLR model had AUC of 0.78 (0.77-0.79), the BART model had AUC of 0.78 (0.77-0.79), and the FLR model had AUC of 0.79 (0.74-0.83). To predict week 52 CS-free remission, the BLR model had AUC of 0.69 (0.68-0.70), the BART model had AUC of 0.69 (0.67-0.70), and the FLR model had AUC of 0.69 (0.64-0.74). The BART model identified nonlinear associations. CONCLUSIONS: BLR and BART models had intuitive interpretation on interval estimation, better precision in estimating the AUC and can be alternatives for predicting clinical outcomes in pediatric patients with UC. BART model can estimate nonlinear nonparametric association.

Design of a pragmatic randomized implementation effectiveness trial testing a health system wide hypertension program for older adults.

Hypertension control remains poor. Multiple barriers at the level of patients, providers, and health systems interfere with implementation of hypertension guidelines and effective lowering of BP. Some strategies such as self-measured blood pressure (SMBP) and remote management by pharmacists are safe and effectively lower BP but have not been effectively implemented. In this study, we combine such evidence-based strategies to build a remote hypertension program and test its effectiveness and implementation in large health systems. This randomized, controlled, pragmatic type I hybrid implementation effectiveness trial will examine the virtual collaborative care clinic (vCCC), a hypertension program that integrates automated patient identification, SMBP, remote BP monitoring by trained health system pharmacists, and frequent patient-provider communication. We will randomize 1000 patients with uncontrolled hypertension from two large health systems in a 1:1 ratio to either vCCC or control (usual care with education) groups for a 2-year intervention. Outcome measures including BP measurements, cognitive function, and a symptom checklist will be completed during study visits. Other outcome measures of cardiovascular events, mortality, and health care utilization will be assessed using Medicare data. For the primary outcome of proportion achieving BP control (defined as systolic BP < 130 mmHg) in the two groups, we will use a generalized linear mixed model analysis. Implementation outcomes include acceptability and feasibility of the program. This study will guide implementation of a hypertension program within large health systems to effectively lower BP.

Stable Mg Single-Atom Solid Base Catalysts Anchored on Metal-Organic Framework-Derived Nitrogen-Doped Carbon.

Solid base catalysts are widely used in the chemical industry owing to their advantages of environmental friendliness and easy separation. However, their application is limited by basic site aggregation and poor stability. In this study, we report the preparation of magnesium (Mg) single-atom catalysts with high activity and stability by a sublimation-trapping strategy. The Mg net was sublimated as Mg vapor at 620 °C, subsequently transported through argon, and finally trapped on the defects of nitrogen-doped carbon derived from metal-organic framework ZIF-8, producing Mg1/NC. Because of the atomically dispersed Mg sites, the obtained Mg1/NC exhibits high catalytic activity and stability for Knoevenagel condensation of benzaldehyde with malononitrile, which is a typical base-catalyzed reaction. The Mg1/NC catalyst achieves a high efficiency with a turnover frequency of 49.6 h-1, which is much better than that of the traditional counterpart MgO/NC (7.7 h-1). In particular, the activity of Mg1/NC shows no decrease after five catalytic cycles, while that of MgO/NC declines due to the instability of basic sites.

TDP-43 was Involved in Radiation-induced Neuronal Damage and May Not Through the BDNF/TrkB Pathway.

Cognitive dysfunction is the most common form of radiation-induced brain injury. TDP-43 is known to be associated with hippocampal degeneration and cognitive dysfunction, in this study we wanted to know if it also had an effect on radiation-induced hippocampus damage. At first, we found the expression of TDP-43 and p-TDP-43 was increased in the hippocampus of rats with radiation-induced cognitive dysfunction. Single-cell RNA-seq analysis of the rat hippocampus showed that TDP-43 was expressed in all cell types and was significantly upregulated in neuron cells after irradiation. Enrichment analysis of gene ontology (GO) functions and KEGG pathways showed that the differential expression genes in neuron after irradiation may be involved in synaptic plasticity. In vitro, the expression of TDP-43 was also increased in neuron cells after irradiation, while the expression of brain-derived neurotrophic factor (BDNF), TrkB, typical synaptic signature proteins (SYN, GAP43 and PSD95), ß-tubulin and dendritic spines were decreased. In the irradiated neurons, the ß-tubulin, dendritic and spines typical synaptic signature proteins had more severe damage in pcDNA3.1-TDP-43 plasmid transfections group, however, the damages were alleviated in the siRNA-TDP-43 plasmid transfections group. BDNF was highly expressed in the irradiated pcDNA3.1-TDP-43 plasmid transfections group, while its expression was decreased in the siRNA-TDP-43 group. The TrkB expression was significantly reduced in neurons after exposure to ionizing radiation, however, there was no significant correlation with TDP-43 expression. These data indicate that TDP-43 is involved in radiation-induced neuronal synaptic plasticity decline and developmental damage, furthermore, the BDNF/TrkB signaling pathway may not be involved in this process.

First screening for tick-borne pathogens in Chinese Milu deer (Elaphurus davidianus).

Ticks are primary vectors for many tick-borne pathogens (TBPs) and pose a serious threat to veterinary and public health. Information on the presence of TBPs in Chinese Milu deer (Elaphurus davidianus) is limited. In this study, a total of 102 Chinese Milu deer blood samples were examined for Anaplasma spp., Theileria spp., Babesia spp., Rickettsia spp., and Borrelia spp., and three TBPs were identified: Anaplasma phagocytophilum (48; 47.1 %), Candidatus Anaplasma boleense (47; 46.1%), and Theileria capreoli (8; 7.8 %). Genetic and phylogenetic analysis of the 16S rRNA and 18S rRNA confirmed their identity with corresponding TBPs. To our knowledge, this is the first report on Candidatus A. boleense and T. capreoli detection in Chinese Milu deer. A high prevalence of A. phagocytophilum with veterinary and medical significance was identified in endangered Chinese Milu deer, which could act as potential zoonotic reservoirs. The identification of the TBPs in Chinese Milu deer provides useful information for the prevention and control of tick-borne diseases.

Positive Airway Pressure Therapy Predicts Lower Mortality and Major Adverse Cardiovascular Events Incidence in Medicare Beneficiaries with Obstructive Sleep Apnea.

Background: Obesity is associated with obstructive sleep apnea (OSA) and cardiovascular risk. Positive airway pressure (PAP) is the first line treatment for OSA, but evidence on its beneficial effect on major adverse cardiovascular events (MACE) prevention is limited. Using claims data, the effects of PAP on mortality and incidence of MACE among Medicare beneficiaries with OSA were examined. Methods: A cohort of Medicare beneficiaries with ≥2 distinct OSA claims was defined from multi-state, state-wide, multi-year (2011-2020) Medicare fee-for-service claims data. Evidence of PAP initiation and utilization was based on PAP claims after OSA diagnosis. MACE was defined as a composite of myocardial infarction, heart failure, stroke, or coronary revascularization. Doubly robust Cox proportional hazards models with inverse probability of treatment weights estimated treatment effects controlling for sociodemographic and clinical factors. Results: Among 888,835 beneficiaries with OSA (median age 73 years; 43.9% women; median follow-up 1,141 days), those with evidence of PAP initiation (32.6%) had significantly lower all-cause mortality (HR [95%CI]: 0.53 [0.52-0.54]) and MACE incidence risk (0.90 [0.89-0.91]). Higher quartiles of annual PAP claims were progressively associated with lower mortality (Q2: 0.84 [0.81-0.87], Q3: 0.76 [0.74-0.79], Q4: 0.74 [0.72-0.77]) and MACE incidence risk (Q2: 0.92 [0.89-0.95], Q3: 0.89 [0.86-0.91], Q4: 0.87 [0.85-0.90]). Conclusion: PAP utilization was associated with lower all-cause mortality and MACE incidence among Medicare beneficiaries with OSA. Results might inform trials assessing the importance of OSA therapy towards minimizing cardiovascular risk and mortality in older adults.

Avenues for Strengthening PCORnet's Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR).

PCORnet, the National Patient-Centered Clinical Research Network, provides the ability to conduct prospective and observational pragmatic research by leveraging standardized, curated electronic health records data together with patient and stakeholder engagement. PCORnet is funded by the Patient-Centered Outcomes Research Institute (PCORI) and is composed of 8 Clinical Research Networks that incorporate at total of 79 health system "sites." As the network developed, linkage to commercial health plans, federal insurance claims, disease registries, and other data resources demonstrated the value in extending the networks infrastructure to provide a more complete representation of patient's health and lived experiences. Initially, PCORnet studies avoided direct economic comparative effectiveness as a topic. However, PCORI's authorizing law was amended in 2019 to allow studies to incorporate patient-centered economic outcomes in primary research aims. With PCORI's expanded scope and PCORnet's phase 3 beginning in January 2022, there are opportunities to strengthen the network's ability to support economic patient-centered outcomes research. This commentary will discuss approaches that have been incorporated to date by the network and point to opportunities for the network to incorporate economic variables for analysis, informed by patient and stakeholder perspectives. Topics addressed include: (1) data linkage infrastructure; (2) commercial health plan partnerships; (3) Medicare and Medicaid linkage; (4) health system billing-based benchmarking; (5) area-level measures; (6) individual-level measures; (7) pharmacy benefits and retail pharmacy data; and (8) the importance of transparency and engagement while addressing the biases inherent in linking real-world data sources.

Sociodemographic Factors Related to Adverse Donor Reactions in Shenzhen.

Background: The adverse donor reaction (ADR) means the uncomfortable feeling felt by blood donors during the whole process of blood donation, which can affect the blood donation behavior of blood donors. So, it is very necessary for blood centers to monitor and prevent it. Methods: Data about ADRs in Shenzhen Blood Center from January 2018 to December 2022 were collected, and correlation analysis was conducted using SPSS 24.0 software. Results: From January 2018 to December 2022, a total of 1265 ADRs occurred in 642,767 blood donations in Shenzhen Blood Center, with an incidence of 0.20%. Most of the ADRs were mild and occurred during blood collections (>90%). The ADR rate of young individuals aged 18-29 years old was the highest (p<0.0001). In addition, a higher ADR rate was observed in first-time blood donors, whole blood donors, and blood donors who donated in the mobile sites (p<0.05). Conclusion: The occurrence of ADRs is related to the sociodemographic factors of blood donors, including age, donation type, donation history, and donation sites. Shenzhen Blood Centers should pay special attention to the process of blood donation among young blood donors aged 18-29 years old, first-time blood donors, whole-blood donors, and blood donors who donate at mobile sites to further reduce the occurrence of ADRs.

Prophylactic Epidural Blood Patch or Prophylactic Epidural Infusion of Hydroxyethyl Starch in Preventing Post-Dural Puncture Headache - A Retrospective Study.

BACKGROUND: Post-dural puncture headache (PDPH) is particularly likely to happen in patients under obstetric care due to an unintentional dural puncture (UDP). There is as yet no ideal strategy for preventing UDP-induced PDPH. OBJECTIVES: The primary objective of this study was to assess whether a prophylactic epidural blood patch (EBP) or prophylactic epidural infusion of hydroxyethyl starch (HES) is effective in preventing PDPH for parturients with UDP compared with conservative treatments. STUDY DESIGN: Retrospective analysis from a single center's inpatient data. SETTING: Department of Anesthesiology at a single center. METHODS: A retrospective study was conducted of a single center's inpatient data from January 2017 through March 2020. The study included parturients with UDP during neuraxial anesthesia. The interventions of UDP included conservative treatment, prophylactic EBP, and prophylactic epidural infusion of HES. The incidence of PDPH, the use of intravenous aminophylline, therapeutic EBP, symptom onset, duration of headache, and duration of hospital stay were compared. RESULTS: A total of 85 patients were analyzed. The incidences of PDPH were 84%, 52.6% and 54.5% with conservative, prophylactic EBP, and prophylactic epidural HES treatments, respectively. Compared with the conservative treatment, prophylactic EBP and prophylactic epidural HES treatment significantly reduced the incidence of PDPH (P < 0.05). No significant difference was found between the prophylactic EBP and prophylactic epidural HES groups. Compared with the conservative treatment group, therapeutic EBP was significantly less used in the prophylactic EBP and prophylactic epidural HES groups (P < 0.05). Prophylactic EBP shortened the length of hospital stay of parturients with UDP (P < 0.05) while prophylactic epidural HES showed no statistical difference compared with conservative treatment. No severe complications, such as central nervous system and puncture site infection or nerve injury, were found in those patients. LIMITATIONS: Retrospective nature and single center data with a relatively small sample size. CONCLUSIONS: Prophylactic management with EBP and epidural infusion of HES has an effect in preventing the occurrence of PDPH; prophylactic EBP significantly shortened hospital stay length in parturients with UDP. KEY WORDS: Unintentional dural puncture, epidural blood patch, hydroxyethyl starch, post-dural puncture headache, parturient.

A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program.

As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS- CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses.

Medications and conditions associated with weight loss in patients prescribed semaglutide based on real-world data.

OBJECTIVE: Approved by the Food and Drug Administration (FDA) in 2017 for diabetes and in 2021 for weight loss, semaglutide has seen widespread use among individuals who aim to lose weight. The aim of this study was to evaluate weight loss and the influence of clinical factors on semaglutide patients in real-world clinical practice. METHODS: Using data from 10 health systems within the Greater Plains Collaborative (a PCORnet Clinical Research Network), nearly 4000 clinical factors encompassing demographic, diagnosis, and prescription information were extracted for semaglutide patients. A gradient-boosting, machine-learning classifier was developed for weight-loss prediction and identification of the most impactful factors via SHapley Additive exPlanations (SHAP) value extrapolation. RESULTS: A total of 3555 eligible patients (539 of whom were observed 52 weeks following exposure) from March 2017 to April 2022 were studied. On average, individuals lost 4.44% (male individuals, 3.66%; female individuals, 5.08%) of their initial weight. History of diabetes mellitus diagnosis was associated with less weight loss, whereas prediabetes and linaclotide use were associated with more pronounced weight loss. CONCLUSIONS: Weight loss in patients prescribed semaglutide from real-world evidence was strong but attenuated compared with previous clinical trials. Machine-learning analysis of electronic health record data identified factors that warrant further research and consideration when tailoring weight-loss therapy.