Transparent and Efficient Wood-Based Triboelectric Nanogenerators for Energy Harvesting and Self-Powered Sensing.

Wood possesses several advantageous qualities including innocuity, low cost, aesthetic appeal, and excellent biocompatibility, and its naturally abundant functional groups and diverse structural forms facilitate functionalization modification. As the most sustainable bio-based material, the combination of wood with triboelectric nanogenerators (TENGs) stands poised to significantly advance the cause of green sustainable production while mitigating the escalating challenges of energy consumption. However, the inherent weak polarizability of natural wood limits its development for TENGs. Herein, we present the pioneering development of a flexible transparent wood-based triboelectric nanogenerator (TW-TENG) combining excellent triboelectrical properties, optical properties, and wood aesthetics through sodium chlorite delignification and epoxy resin impregnation. Thanks to the strong electron-donating groups in the epoxy resin, the TW-TENG obtained an open-circuit voltage of up to ~127 V, marking a remarkable 530% enhancement compared to the original wood. Furthermore, durability and stability were substantiated through 10,000 working cycles. In addition, the introduction of epoxy resin and lignin removal endowed the TW-TENG with excellent optical characteristics, with optical transmittance of up to 88.8%, while preserving the unique texture and aesthetics of the wood completely. Finally, we show the application prospects of TW-TENGs in the fields of self-power supply, motion sensing, and smart home through the demonstration of a TW-TENG in the charging and discharging of capacitors and the output of electrical signals in different scenarios.

The efficacy and safety of short-term and low-dose IL-2 combined with tocilizumab to treat rheumatoid arthritis.

Background: Immunotherapy targeting factors related to immune imbalance has been widely employed for RA treatment. This study aimed to evaluate the efficacy and safety of low-dose interleukin (IL)-2 combined with tocilizumab (TCZ), a biologics targeting IL-6, in RA patients. Methods: Fifty adults with active RA who met the criteria with complete clinical data were recruited, and divided into three groups: control group (n=15), IL-2 group (n=26), and IL-2+TCZ group (n=9). In addition to basic treatment, participants in the IL-2 group received IL-2 (0.5 MIU/day), while participants in the IL-2+TCZ group received IL-2 (0.5 MIU/day) along with one dose of TCZ (8 mg/kg, maximum dose: 800 mg). All subjects underwent condition assessment, laboratory indicators and safety indicators detection, and records before treatment and one week after treatment. Results: Compared with the baseline, all three groups showed significant improvement in disease conditions, as evidenced by significantly reduced disease activity indicators. The low-dose IL-2 and combination treatment groups demonstrated a violent proliferation of Tregs, while the absolute number of Th1, Th2, and Th17 cells in the latter group showed a decreasing trend. The decrease in the Th17/Treg ratio was more pronounced in the IL-2+TCZ groups. No significant adverse reactions were observed in any of the patients. Conclusion: Exogenous low doses of IL-2 combined TCZ were found to be safe and effective in reducing effector T cells and appropriately increasing Treg levels in RA patients with high effector T cell levels. This approach helps regulate immune homeostasis and contributes to the prevention of disease deterioration. Clinical trial registration: https://www.chictr.org.cn/showprojEN.html?proj=13909, identifier ChiCTR-INR-16009546.

Childhood screening for type 1 diabetes comparing automated multiplex Antibody Detection by Agglutination-PCR (ADAP) with single plex islet autoantibody radiobinding assays.

BACKGROUND: Two or more autoantibodies against either insulin (IAA), glutamic acid decarboxylase (GADA), islet antigen-2 (IA-2A) or zinc transporter 8 (ZnT8A) denote stage 1 (normoglycemia) or stage 2 (dysglycemia) type 1 diabetes prior to stage 3 type 1 diabetes. Automated multiplex Antibody Detection by Agglutination-PCR (ADAP) assays in two laboratories were compared to single plex radiobinding assays (RBA) to define threshold levels for diagnostic specificity and sensitivity. METHODS: IAA, GADA, IA-2A and ZnT8A were analysed in 1504 (54% females) population based controls (PBC), 456 (55% females) doctor's office controls (DOC) and 535 (41% females) blood donor controls (BDC) as well as in 2300 (48% females) patients newly diagnosed (1-10 years of age) with stage 3 type 1 diabetes. The thresholds for autoantibody positivity were computed in 100 10-fold cross-validations to separate patients from controls either by maximizing the χ2-statistics (chisq) or using the 98th percentile of specificity (Spec98). Mean and 95% CI for threshold, sensitivity and specificity are presented. FINDINGS: The ADAP ROC curves of the four autoantibodies showed comparable AUC in the two ADAP laboratories and were higher than RBA. Detection of two or more autoantibodies using chisq showed 0.97 (0.95, 0.99) sensitivity and 0.94 (0.91, 0.97) specificity in ADAP compared to 0.90 (0.88, 0.95) sensitivity and 0.97 (0.94, 0.98) specificity in RBA. Using Spec98, ADAP showed 0.92 (0.89, 0.95) sensitivity and 0.99 (0.98, 1.00) specificity compared to 0.89 (0.77, 0.86) sensitivity and 1.00 (0.99, 1.00) specificity in the RBA. The diagnostic sensitivity and specificity were higher in PBC compared to DOC and BDC. INTERPRETATION: ADAP was comparable in two laboratories, both comparable to or better than RBA, to define threshold levels for two or more autoantibodies to stage type 1 diabetes. FUNDING: Supported by The Leona M. and Harry B. Helmsley Charitable Trust (grant number 2009-04078), the Swedish Foundation for Strategic Research (Dnr IRC15-0067) and the Swedish Research Council, Strategic Research Area (Dnr 2009-1039). AL was supported by the DiaUnion collaborative study, co-financed by EU Interreg ÖKS, Capital Region of Denmark, Region Skåne and the Novo Nordisk Foundation.

Significant Association of Serum Albumin With the Severity of Coronary Microvascular Dysfunction Using Dynamic CZT-SPECT.

OBJECTIVE: Both low serum albumin (SA) concentration and coronary microvascular dysfunction (CMD) are risk factors for the development of heart failure (HF). We hypothesized that SA concentration is associated with myocardial flow reserve (MFR) and implicated in pathophysiological mechanism of HF. METHODS: We retrospectively studied 454 patients undergoing dynamic cardiac cadmium-zinc-telluride myocardial perfusion imaging from April 2018 to February 2020. The population was categorized into three groups according to SA level (g/dL): Group 1: >4, Group 2: 3.5-4, and Group 3: <3.5. Myocardial blood flow (MBF) and myocardial flow reserve (MFR, defined as stress/rest MBF ratio) were compared. RESULTS: The mean age of the whole cohort was 66.2 years, and 65.2% were men. As SA decreased, stress MBF (mL min-1 g-1) and MFR decreased (MBF: 3.29 ± 1.03, MFR: 3.46 ± 1.33 in Group 1, MBF: 2.95 ± 1.13, MFR: 2.51 ± 0.93 in Group 2, and MBF: 2.64 ± 1.16, MFR: 1.90 ± 0.50 in Group 3), whereas rest MBF (mL min-1 g-1) increased (MBF: 1.05 ± 0.42 in Group 1, 1.27 ± 0.56 in Group 2, and 1.41 ± 0.61 in Group 3). After adjusting for covariates, compared with Group 1, the odds ratios for impaired MFR (defined as MFR < 2.5) were 3.57 (95% CI: 2.32-5.48) for Group 2 and 34.9 (95% CI: 13.23-92.14) for Group 3. The results would be similar if only regional MFR were assessed. The risk prediction for CMD using SA was acceptable, with an AUC of 0.76. CONCLUSION: Low SA concentration was associated with the severity of CMD in both global and regional MFR as well as MBF.

Augmin complex activity finetunes dendrite morphology through non-centrosomal microtubule nucleation in vivo.

During development, neurons achieve a stereotyped neuron type-specific morphology, which relies on dynamic support by microtubules (MTs). An important player is the augmin complex (hereafter augmin), which binds to existing MT filaments and recruits the γ-tubulin ring complex (γ-TuRC), to form branched MTs. In cultured neurons, augmin is important for neurite formation. However, little is known about the role of augmin during neurite formation in vivo. Here, we have revisited the role of mammalian augmin in culture and then turned towards the class four Drosophila dendritic arborization (c4da) neurons. We show that MT density is maintained through augmin in cooperation with the γ-TuRC in vivo. Mutant c4da neurons show a reduction of newly emerging higher-order dendritic branches and in turn also a reduced number of their characteristic space-filling higher-order branchlets. Taken together, our data reveal a cooperative function for augmin with the γ-TuRC in forming enough MTs needed for the appropriate differentiation of morphologically complex dendrites in vivo.

Utilizing Deep Learning and Computed Tomography to Determine Pulmonary Nodule Activity in Patients With Nontuberculous Mycobacterial-Lung Disease.

PURPOSE: To develop and evaluate a deep convolutional neural network (DCNN) model for the classification of acute and chronic lung nodules from nontuberculous mycobacterial-lung disease (NTM-LD) on computed tomography (CT). MATERIALS AND METHODS: We collected a data set of 650 nodules (316 acute and 334 chronic) from the CT scans of 110 patients with NTM-LD. The data set was divided into training, validation, and test sets in a ratio of 4:1:1. Bounding boxes were used to crop the 2D CT images down to the area of interest. A DCNN model was built using 11 convolutional layers and trained on these images. The performance of the model was evaluated on the hold-out test set and compared with that of 3 radiologists who independently reviewed the images. RESULTS: The DCNN model achieved an area under the receiver operating characteristic curve of 0.806 for differentiating acute and chronic NTM-LD nodules, corresponding to sensitivity, specificity, and accuracy of 76%, 68%, and 72%, respectively. The performance of the model was comparable to that of the 3 radiologists, who had area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy of 0.693 to 0.771, 61% to 82%, 59% to 73%, and 60% to 73%, respectively. CONCLUSIONS: This study demonstrated the feasibility of using a DCNN model for the classification of the activity of NTM-LD nodules on chest CT. The model performance was comparable to that of radiologists. This approach can potentially and efficiently improve the diagnosis and management of NTM-LD.

Efficacy and safety of bimekizumab in the treatment of psoriatic arthritis: a systematic review and meta-analysis.

BACKGROUND: Bimekizumab, a humanized monoclonal IgG1 antibody targeting both interleukin (IL)-17A and IL-17F, could be effective for treating Psoriatic arthritis (PsA). This study aimed to systematically evaluate the efficacy and safety of bimekizumab in the management of PsA. RESEARCH DESIGN AND METHODS: A comprehensive literature search by August 2023 was performed through PubMed, Embase, Cochrane Controlled Register of Trials, and ClinicalTrials.gov. investigating the efficacy or safety data of bimekizumab in the treatment of PsA. Data was pooled using the random-effects models. Egger tests were used to evaluate potential publication bias. RESULTS: A total of 4 RCTs, involving 892 PsA patients and 467 placebo controls, were included in this analysis. Bimekizumab significantly increased the rates of PASI75 and PASI100 compared with placebos [RR = 7.22, 95% CI (5.24, 9.94), p < 0.001; RR = 10.12, 95% CI (6.00, 17.09), p < 0.001]. The rate of overall adverse events was slightly higher in the bimekizumab group [RR = 1.42, 95% CI (1.05, 1.93) p = 0.023). However, there were fewer adverse severe drug reactions in the bimekizumab group compared to the placebo. CONCLUSION: Bimekizumab had a significant clinical benefit in managing PsA and an acceptable safety profile.

Stacking architecture for collimated backlight using cylindrical lens sheet with linear light sources or edge-lit/direct-lit BLU.

Highly collimated and directional backlights are essential for realizing advanced display technologies such as autostereoscopic 3D displays. Previously reported collimated backlights, either edge-lit or direct-lit, in general still suffer unsatisfactory form factors, directivity, uniformity, or crosstalk etc. In this work, we report a simple stacking architecture for the highly collimated and uniform backlights, by combining linear light source arrays and carefully designed cylindrical lens arrays. Experiments were conducted to validate the design and simulation, using the conventional edge-lit backlight or the direct-lit mini-LED (mLED) arrays as light sources, the NiFe (stainless steel) barrier sheets, and cylindrical lens arrays fabricated by molding. Highly collimated backlights with small angular divergence of ±1.45°âˆ¼±2.61°, decent uniformity of 93-96%, and minimal larger-angle sidelobes in emission patterns were achieved with controlled divergence of the light source and optimization of lens designs. The architecture reported here provides a convenient way to convert available backlight sources into a highly collimated backlight, and the use of optically reflective barrier also helps recycle light energy and enhance the luminance. The results of this work are believed to provide a facile approach for display technologies requiring highly collimated backlights.

Exploring Experiences of Conflict within Medical Teams in an Emergency Department: A Focus Group Approach during the COVID-19 Pandemic.

The factors related to conflicts in emergency departments (EDs) have been studied for decades. The post-pandemic digital era may transform the medical landscape in EDs, potentially changing the patterns of conflict between healthcare professionals. This study used focus group interviews to explore conflicts in EDs. Four groups, each with 4-6 participants, took part in this study. Semi-structured interviews were conducted using six research questions. Summative content analysis was used to analyze the data. The participant's average age was 37.82 years, and the average number of working years was 12.12. The following five themes emerged: multiple patterns of internal conflict; external conflicts arising from cross-departmental coordination; conflicts due to unclear job boundaries; adapting to conflicts in diverse ways; and seeking hospital arbitration. The results of this study suggest extending interdisciplinary collaborative practice from emergency departments to all coordinating departments. An inclusive environment for equality between professions and open communication should be promoted by hospitals.

TransGEM: a molecule generation model based on Transformer with gene expression data.

MOTIVATION: It is difficult to generate new molecules with desirable bioactivity through ligand-based de novo drug design, and receptor-based de novo drug design is constrained by disease target information availability. The combination of artificial intelligence and phenotype-based de novo drug design can generate new bioactive molecules, independent from disease target information. Gene expression profiles can be used to characterize biological phenotypes. The Transformer model can be utilized to capture the associations between gene expression profiles and molecular structures due to its remarkable ability in processing contextual information. RESULTS: We propose TransGEM (Transformer-based model from gene expression to molecules), which is a phenotype-based de novo drug design model. A specialized gene expression encoder is used to embed gene expression difference values between diseased cell lines and their corresponding normal tissue cells into TransGEM model. The results demonstrate that the TransGEM model can generate molecules with desirable evaluation metrics and property distributions. Case studies illustrate that TransGEM model can generate structurally novel molecules with good binding affinity to disease target proteins. The majority of genes with high attention scores obtained from TransGEM model are associated with the onset of the disease, indicating the potential of these genes as disease targets. Therefore, this study provides a new paradigm for de novo drug design, and it will promote phenotype-based drug discovery. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/hzauzqy/TransGEM.

Genetically determined type 1 diabetes mellitus and risk of osteoporosis.

BACKGROUND: Observational evidence suggests that type 1 diabetes mellitus (T1DM) is associated with the risk of osteoporosis (OP). Nevertheless, it is not apparent whether these correlations indicate a causal relationship. To elucidate the causal relationship, a two-sample Mendelian randomization (MR) analysis was performed. METHODS: T1DM data was obtained from the large genome-wide association study (GWAS), in which 6683 cases and 12,173 controls from 12 European cohorts were involved. Bone mineral density (BMD) samples at four sites were extracted from the GEnetic Factors for OSteoporosis (GEFOS) consortium, including forearm (FA) (n = 8,143), femoral neck (FN) (n = 32,735), lumbar spine (LS) (n = 28,498), and heel (eBMD) (n = 426,824). The former three samples were from mixed populations and the last one was from European. Inverse variance weighting, MR-Egger, and weighted median tests were used to test the causal relationship between T1DM and OP. A series of sensitivity analyses were then conducted to verify the robustness of the results. RESULTS: Twenty-three independent SNPs were associated with FN-BMD and LS-BMD, twenty-seven were associated with FA-BMD, and thirty-one were associated with eBMD. Inverse variance-weighted estimates indicated a causal effect of T1DM on FN-BMD (odds ratio (OR) =1.033, 95 % confidence interval (CI): 1.012-1.054, p = 0.002) and LS-BMD (OR = 1.032, 95 % CI: 1.005-1.060, p = 0.022) on OP risk. Other MR methods, including weighted median and MR-Egger, calculated consistent trends. While no significant causation was found between T1DM and the other sites (FA-BMD: OR = 1.008, 95 % CI: 0.975-1.043, p = 0.632; eBMD: OR = 0.993, 95 % CI: 0.985-1.001, p = 0.106). No significant heterogeneity (except for eBMD) or horizontal pleiotropy was found for instrumental variables, suggesting these results were reliable and robust. CONCLUSIONS: This study shows a causal relationship between T1DM and the risk of some sites of OP (FN-BMD, LS-BMD), allowing for continued research to discover the clinical and experimental mechanisms of T1DM and OP. It also contributes to the recommendation if patients with T1DM need targeted care to promote bone health and timely prevention of osteoporosis.

The relationship between a series of inflammatory markers on the risk of heart failure in different gender groups: Analysis from NHANES 2015-2018.

BACKGROUND: A better understanding of the level-grade inflammation for the development and worsening of heart failure (HF) in different gender groups is an unmet need. We performed an updated analysis on the impact of a series of systemic inflammation markers on HF. METHODS: This compensatory cross-sectional study enrolled participants from the National Health and Nutrition Examination Survey (NHANES) 2015-2018. HF was based on the self-reported questions. Univariate and multivariate logistic regression were used to investigate the association between systemic immune-inflammation index (SII), high sensitivity C-reactive protein (hs-CRP), lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and HF. For patients of different genders, P for trend was used to analyze potential linear trend relationships and the restricted cubic splines (RCS) were used to describe non-linear relationships. The additive interaction was evaluated by the relative excess risk due to interaction (RERI), attributable proportion (AP), and the synergy index (SI). The multiplicative interaction was evaluated by odds ratio (OR) and 95% confidence interval (CI) of product-term. RESULTS: A total of 5,830 participants from the NHANES database were divided into two groups: the HF group (n = 210) and the non-HF group (n = 5620). After gender stratification, hs-CRP (OR: 1.01, 95% CI: 1.00-1.03), SII (OR: 1.00, 95% CI: 1.00-1.01), NLR (OR: 1.22, 95% CI: 1.11-1.35) and LMR (OR: 0.79, 95% CI: 0.65-0.93) were independent meaningful factors for HF in males, there was no non-linear relationship between the three factors (SII, NLR, hs-CRP, all P for non-linear > 0.05) and the prevalence of HF, but we detected a non-linear relationship between LMR and the prevalence of HF in males (P for non-linear < 0.05). An additive interaction of hs-CRP and NLR on the risk of HF in males (RERI (OR): 0.67, 95% CI: 0.12-1.34; AP (OR): 0.14, 95% CI: 0.02-0.24; SI (OR): 1.22, 95% CI: 1.03-1.44). CONCLUSIONS: In summary, hs-CRP, NLR, and LMR are superior meaningful markers for HF in males. SII may be a meaningful systemic inflammation warning marker for HF, which needs to be discriminated against with caution. Only detected a non-linear relationship between LMR and the prevalence of HF in males. NLR and hs-CRP may have an additive interaction in the prevalence of male HF patients. The outcome compensated for previous studies that still needed more studies for validation.

Thoracolumbar Interfascial Plane (TLIP) block verses other paraspinal fascial plane blocks and local infiltration for enhanced pain control after spine surgery: a systematic review.

Spinal surgeries are accompanied by excessive pain due to extensive dissection and muscle retraction during the procedure. Thoracolumbar interfascial plane (TLIP) blocks for spinal surgeries are a recent addition to regional anesthesia to improve postoperative pain management. When performing a classical TLIP (cTLIP) block, anesthetics are injected between the muscle (m.) multifidus and m. longissimus. During a modified TLIP (mTLIP) block, anesthetics are injected between the m. longissimus and m. iliocostalis instead. Our systematic review provides a comprehensive evaluation of the effectiveness of TLIP blocks in improving postoperative outcomes in spinal surgery through an analysis of randomized controlled trials (RCTs).We conducted a systematic review based on the PRISMA guidelines using PubMed and Scopus databases. Inclusion criteria required studies to be RCTs in English that used TLIP blocks during spinal surgery and report both outcome measures. Outcome data includes postoperative opioid consumption and pain.A total of 17 RCTs were included. The use of a TLIP block significantly decreases postoperative opioid use and pain compared to using general anesthesia (GA) plus 0.9% saline with no increase in complications. There were mixed outcomes when compared against wound infiltration with local anesthesia. When compared with erector spinae plane blocks (ESPB), TLIP blocks often decreased analgesic use, however, this did not always translate to decreased pain. The cTLIP and mTLP block methods had comparable postoperative outcomes but the mTLIP block had a significantly higher percentage of one-time block success.The accumulation of the current literature demonstrates that TLIP blocks are superior to non-block procedures in terms of analgesia requirements and reported pain throughout the hospitalization in patients who underwent spinal surgery. The various levels of success seen with wound infiltration and ESPB could be due to the nature of the different spinal procedures. For example, studies that saw superiority with TLIP blocks included fusion surgeries which is a more invasive procedure resulting in increased postoperative pain compared to discectomies.The results of our systematic review include moderate-quality evidence that show TLIP blocks provide effective pain control after spinal surgery. Although, the application of mTLIP blocks is more successful, more studies are needed to confirm that superiority of mTLIP over cTLIP blocks. Additionally, further high-quality research is needed to verify the potential benefit of TLIP blocks as a common practice for spinal surgeries.

Preclinical systolic dysfunction relating to ankle-brachial index among high-risk PAD population with preserved left ventricular ejection fraction.

Peripheral artery disease (PAD) shares common clinical risk factors, for example, endothelial dysfunction, with preserved ejection fraction (LVEF) heart failure (HFpEF). Whether PAD is associated with preclinical systolic dysfunction and higher HF risk among individuals presenting preserved LVEF remains uncertain. We retrospectively included outpatients with at least one known or established cardiovascular (CV) risk factor with LVEF ≥ 50%. Patients were categorized into high risk and low risk of developing PAD (PAD vs Non-PAD) by ankle-brachial index (ABI) (≤ 0.90 or > 1.4) and further stratified based on their history of HFpEF (HFpEF vs. Non-HFpEF), resulting in the formation of four distinct strata. Preclinical systolic dysfunction was defined using dedicated speckle-tracking algorithm. A total of 2130 consecutive patients were enrolled in the study, with a median follow-up of 4.4 years. The analysis revealed a higher prevalence of high risk of developing PAD in patients with HFpEF compared to those without HFpEF (25.1% vs. 9.4%). Both high risk of developing PAD and HFpEF were independently associated with preclinical systolic dysfunction (global longitudinal strain, GLS ≥ - 18%) (odds ratio, OR: 1.38; 95% confidence interval, CI: 1.03-1.86). In comparison to patients at low risk of developing PAD without HFpEF (Non-PAD/Non-HFpEF group), those categorized as having a high risk of developing PAD with HFpEF (PAD/HFpEF group) exhibited the most impaired GLS and a heightened susceptibility to heart failure hospitalization (hazard ratio, HR: 6.51; 95% CI: 4.43-9.55), a twofold increased risk of all-cause mortality (HR: 2.01; 95% CI: 1.17-3.38), cardiovascular mortality (HR: 2.44; 95% CI: 1.08-5.51), and non-cardiovascular mortality (HR: 1.78; 95% CI: 0.82-3.84). A high risk of developing PAD was strongly linked to impaired preclinical systolic function and an increased likelihood for subsequent hospitalization for HF, all-cause mortality, CV mortality and non-CV mortality. There is a clear need for preventive strategies aimed at reducing hospitalizations for HF and mortality in this high-risk population.

Contrasting PRRSV temporal lineage patterns at the individual farm, production system, and regional levels in Ohio and neighboring states from 2017 to 2021.

Porcine reproductive and respiratory virus (PRRSV), one of the most significant viruses in the swine industry, has been challenging to control due to its high mutation and recombination rates and complexity. This retrospective study aimed to describe and compare the distribution of PRRSV lineages obtained at the individual farm, production system, and regional levels. PRRSV-2 (type 2) sequences (n = 482) identified between 2017 - 2021 were provided by a regional state laboratory (Ohio Department of Agriculture, Animal Disease Diagnostic Center (ODA-ADDL)) collected from swine farms in Ohio and neighboring states, including Indiana, Michigan, Pennsylvania, and West Virginia. Additional sequences (n = 138) were provided by one collaborating swine production system. The MUSCLE algorithm on Geneious Prime® was used to align the ORF5 region of PRRSV-2 sequences along with PRRSV live attenuated vaccine strains (n = 6) and lineage anchors (n = 169). Sequenced PRRSV-2 were assigned to the most identical lineage anchors/vaccine strains. Among all sequences (n = 620), 29.8% (185/620) were ≥ 98.0% identity with the vaccine strains, where 93.5% (173/185) and 6.5% (12/185) were identical with the L5 Ingelvac PRRS® MLV and L8 Fostera® PRRS vaccine strains, respectively, and excluded from the analysis. At the regional level across five years, the top five most identified lineages included L1A, L5, L1H, L1C, and L8. Among non-vaccine sequences with production system known, L1A sequences were mostly identified (64.3% - 100.0%) in five systems, followed by L1H (0.0% - 28.6%), L1C (0.0% - 10.5%), L5 (0.0% - 14.4%), L8 (0.0% - 1.3%), and L1F (0.0% - 0.5%). Furthermore, among non-vaccine sequences with the premise identification available (n = 262), the majority of sequences from five individual farms were either classified into L1A or L5. L1A and L5 sequences coexisted in three farms, while samples submitted by one farm contained L1A, L1H, and L5 sequences. Additionally, the lineage classification results of non-vaccine sequences were associated with their restriction fragment length polymorphism (RFLP) patterns (Fisher's exact test, p < 0.05). Overall, our results show that individual farm and production system-level PRRSV-2 lineage patterns do not necessarily correspond to regional-level patterns, highlighting the influence of individual farms and systems in shaping PRRSV occurrence within those levels, and highlighting the crucial goal of within-farm and system monitoring and early detection for accurate knowledge on PRRSV-2 lineage occurrence and emergence.

Investigating the uses of machine learning algorithms to inform risk factor analyses: The example of avian infectious bronchitis virus (IBV) in broiler chickens.

Infectious bronchitis virus (IBV) is a contagious coronavirus causing respiratory and urogenital disease in chickens and is responsible for significant economic losses for both the broiler and table egg layer industries. Despite IBV being regularly monitored using standard epidemiologic surveillance practices, knowledge and evidence of risk factors associated with IBV transmission remain limited. The study objective was to compare risk factor modeling outcomes between a traditional stepwise variable selection approach and a machine learning-based random forest Boruta algorithm using routinely collected IBV antibody titer data from broiler flocks. IBV antibody sampling events (n = 1111) from 166 broiler sites between 2016 and 2021 were accessed. Ninety-two geospatial-related and poultry-density variables were obtained using a geographic information system and data sets from publicly available sources. Seventeen and 27 candidate variables were screened to potentially have an association with elevated IBV antibody titers according to the manual selection and machine learning algorithm, respectively. Selected variables from both methods were further investigated by construction of multivariable generalized mixed logistic regression models. Six variables were shortlisted by both screening methods, which included year, distance to urban areas, main roads, landcover, density of layer sites and year, however, final models for both approaches only shared year as an important predictor. Despite limited significance of clinical outcomes, this work showcases the potential of a novel explorative modeling approach in combination with often unutilized resources such as publicly available geospatial data, surveillance health data and machine learning as potential supplementary tools to investigate risk factors related to infectious diseases.

Pattern classification of interstitial lung diseases from computed tomography images using a ResNet-based network with a split-transform-merge strategy and split attention.

In patients with interstitial lung disease (ILD), accurate pattern assessment from their computed tomography (CT) images could help track lung abnormalities and evaluate treatment efficacy. Based on excellent image classification performance, convolutional neural networks (CNNs) have been massively investigated for classifying and labeling pathological patterns in the CT images of ILD patients. However, previous studies rarely considered the three-dimensional (3D) structure of the pathological patterns of ILD and used two-dimensional network input. In addition, ResNet-based networks such as SE-ResNet and ResNeXt with high classification performance have not been used for pattern classification of ILD. This study proposed a SE-ResNeXt-SA-18 for classifying pathological patterns of ILD. The SE-ResNeXt-SA-18 integrated the multipath design of the ResNeXt and the feature weighting of the squeeze-and-excitation network with split attention. The classification performance of the SE-ResNeXt-SA-18 was compared with the ResNet-18 and SE-ResNeXt-18. The influence of the input patch size on classification performance was also evaluated. Results show that the classification accuracy was increased with the increase of the patch size. With a 32 × 32 × 16 input, the SE-ResNeXt-SA-18 presented the highest performance with average accuracy, sensitivity, and specificity of 0.991, 0.979, and 0.994. High-weight regions in the class activation maps of the SE-ResNeXt-SA-18 also matched the specific pattern features. In comparison, the performance of the SE-ResNeXt-SA-18 is superior to the previously reported CNNs in classifying the ILD patterns. We concluded that the SE-ResNeXt-SA-18 could help track or monitor the progress of ILD through accuracy pattern classification.

Synthesis Landscapes for Ammonia Borane Chemical Vapor Deposition of h-BN and BNNT: Unraveling Reactions and Intermediates from First-Principles.

Planar hexagonal boron nitride (h-BN) and tubular BN nanotube (BNNT), known for their superior mechanical and thermal properties, as well as wide electronic band gap, hold great potential for nanoelectronic and optoelectronic devices. Chemical vapor deposition has demonstrated the best way to scalable synthesis of high-quality BN nanomaterials. Yet, the atomistic understanding of reactions from precursors to product-material remains elusive, posing challenges for experimental design. Here, performing first-principles calculations and ab initio molecular simulations, we explore pyrolytic decomposition pathways of the most used precursor ammonia borane (H3BNH3, AB) to BN, in gas-phase and on Ni(111) or amorphous boron (for BNNT growth) surfaces, for comparison. It reveals that in the gas phase, a pair of AB molecules cooperate to form intermediate NH3 and ammonia diborane, which further dissociates into H2BNH2, accompanied by critical BH4- and NH4+ ions. These ions act as H scavengers facilitating H2BNH2 dehydrogenation into HBNH. The consequent HBNH directly feeds BN flake growth by reacting with the crystal edge, while the addition of H2BNH2 to the edge is prohibited at 1500 K. In contrast, on Ni and boron surfaces, AB monomer dehydrogenates stepwise, deeper, yielding BNH and BN dimer as the primary building unit. Our study maps out three typical experimental conditions regarding the dissociation of AB-precursor, providing insights into the underlying reaction mechanisms of gas-phase precursors, to help as guidelines for the experimental growth of BN nanomaterials.

Anxiety, Depression and Quality of Life in Relation to SARS-CoV-2 Antibodies in Individuals Living with Diabetes During the Second Wave of COVID-19.

Aims: The objective was to compare anxiety, depression, and quality of life (QoL) in individuals living with type 1 (T1D) and type 2 (T2D) diabetes with matched controls during the second wave of the COVID-19 pandemic. Methods: Via randomization, individuals living with diabetes T1D (n= 203) and T2D (n=413), were identified during February-July 2021 through health-care registers. Population controls (n=282) were matched for age, gender, and residential area. Questionnaires included self-assessment of anxiety, depression, QoL, and demographics in relation to SARS-CoV-2 exposure.Blood was collected through home-capillary sampling, and SARS-CoV-2 Nucleocapsid (NCP) and Spike antibodies (SC2_S1) were determined by multiplex Antibody Detection by Agglutination-PCR (ADAP) assays. Results: Younger age and health issues were related to anxiety, depression, and QoL, with no differences between the study groups. Female gender was associated with anxiety, while obesity was associated with lower QoL.The SARS-CoV-2 NCP seroprevalence was higher in T1D (8.9%) compared to T2D (3.9%) and controls (4.0%), while the SARS-CoV-2 SC2_S1 seroprevalence was higher for controls (25.5%) compared to T1D (16.8%) and T2D (14.0%). Conclusions: A higher SARS-CoV-2 infection rate in T1D may be explained by younger age and higher employment rate, and the associated increased risk for viral exposure.

Accelerating Antimicrobial Peptide Discovery for WHO Priority Pathogens through Predictive and Interpretable Machine Learning Models.

The escalating menace of multidrug-resistant (MDR) pathogens necessitates a paradigm shift from conventional antibiotics to innovative alternatives. Antimicrobial peptides (AMPs) emerge as a compelling contender in this arena. Employing in silico methodologies, we can usher in a new era of AMP discovery, streamlining the identification process from vast candidate sequences, thereby optimizing laboratory screening expenditures. Here, we unveil cutting-edge machine learning (ML) models that are both predictive and interpretable, tailored for the identification of potent AMPs targeting World Health Organization's (WHO) high-priority pathogens. Furthermore, we have developed ML models that consider the hemolysis of human erythrocytes, emphasizing their therapeutic potential. Anchored in the nuanced physical-chemical attributes gleaned from the three-dimensional (3D) helical conformations of AMPs, our optimized models have demonstrated commendable performance-boasting an accuracy exceeding 75% when evaluated against both low-sequence-identified peptides and recently unveiled AMPs. As a testament to their efficacy, we deployed these models to prioritize peptide sequences stemming from PEM-2 and subsequently probed the bioactivity of our algorithm-predicted peptides vis-à-vis WHO's priority pathogens. Intriguingly, several of these new AMPs outperformed the native PEM-2 in their antimicrobial prowess, thereby underscoring the robustness of our modeling approach. To elucidate ML model outcomes, we probe via Shapley Additive exPlanations (SHAP) values, uncovering intricate mechanisms guiding diverse actions against bacteria. Our state-of-the-art predictive models expedite the design of new AMPs, offering a robust countermeasure to antibiotic resistance. Our prediction tool is available to the public at https://ai-meta.chem.ncu.edu.tw/amp-meta.