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In preclinical studies, a new antituberculosis drug regimen markedly reduced the time required to achieve relapse-free cure. This study aimed to preliminarily evaluate the efficacy and safety of this four-month regimen, consisting of clofazimine, prothionamide, pyrazinamide and ethambutol, with a standard six-month regimen in patients with drug-susceptible tuberculosis. An open-label pilot randomized clinical trial was conducted among the patients with newly diagnosed bacteriologically-confirmed pulmonary tuberculosis. The primary efficacy end-point was sputum culture negative conversion. Totally, 93 patients were included in the modified intention-to-treat population. The rates of sputum culture conversion were 65.2% (30/46) and 87.2% (41/47) for short-course and standard regimen group, respectively. There was no difference on two-month culture conversion rates, time to culture conversion, nor early bactericidal activity (P > 0.05). However, patients on short-course regimen were observed with lower rates of radiological improvement or recovery and sustained treatment success, which was mainly attributed to higher percent of patients permanently changed assigned regimen (32.1% vs. 12.3%, P = 0.012). The main cause for it was drug-induced hepatitis (16/17). Although lowering the dose of prothionamide was approved, the alternative option of changing assigned regimen was chosen in this study. While in per-protocol population, sputum culture conversion rates were 87.0% (20/23) and 94.4% (34/36) for the respective groups. Overall, the short-course regimen appeared to have inferior efficacy and higher incidence of hepatitis but desired efficacy in per-protocol population. It provides the first proof-of-concept in humans of the capacity of the short-course approach to identify drug regimens that can shorten the treatment time for tuberculosis.

Sepsis is a life-threatening condition characterized by an exaggerated immune response to pathogens, leading to organ damage and high mortality rates in the intensive care unit. Although deep learning has achieved impressive performance on prediction and classification tasks in medicine, it requires large amounts of data and lacks explainability, which hinder its application to sepsis diagnosis. We introduce a deep learning framework, called scCaT, which blends the capsulating architecture with Transformer to develop a sepsis diagnostic model using single-cell RNA sequencing data and transfers it to bulk RNA data. The capsulating architecture effectively groups genes into capsules based on biological functions, which provides explainability in encoding gene expressions. The Transformer serves as a decoder to classify sepsis patients and controls. Our model achieves high accuracy with an AUROC of 0.93 on the single-cell test set and an average AUROC of 0.98 on seven bulk RNA cohorts. Additionally, the capsules can recognize different cell types and distinguish sepsis from control samples based on their biological pathways. This study presents a novel approach for learning gene modules and transferring the model to other data types, offering potential benefits in diagnosing rare diseases with limited subjects. Author summary: Deep learning models used in disease diagnosis usually suffer from insufficient data for training and the lack of explainability, especially in rare diseases. These shortages hinder their application to sepsis diagnosis. Here we propose a diagnostic framework name scCaT, which transfers knowledge learned from single-cell RNA-seq, for diseases with insufficient bulk data. The framework uses capsulating architecture to group genes into capsules and provide explainability to the deep learning model for sepsis diagnosis. ScCaT achieves robust and outstanding performance for sepsis diagnosis in both scRNA-seq and bulk RNA datasets. This architecture offers potential approaches in diagnosing rare diseases with limited subjects with explainability. [ABSTRACT FROM AUTHOR]

Daily monitoring of psychological parameters, encompassing chemical and physical signals, has become increasingly valuable for the management of chronic diseases. Among various vital signs, carbon dioxide exchange and pulse rate (PR) serve as critical indicators for the comprehensive assessment of the cardiopulmonary system. However, simultaneous monitoring of these chemical and physical signals in a nonintrusive manner is challenging. This study presents a wearable bracelet that enables simultaneous monitoring of transcutaneous carbon dioxide (TcCO2) and PR using a nanomaterials‐modified lensless CMOS imager. The proposed bracelet exhibits exceptional sensitivity, selectivity, and anti‐humidity interference in TcCO2 detection, and achieves reliable tracking of TcCO2 and PR fluctuations induced by daily activities, demonstrating great potential in the comprehensive assessment of cardiopulmonary system. In addition, the proposed compact imaging approach is multiplexed and extensible, providing a promising sensing platform for the future development of multifunctional wearable devices. [ABSTRACT FROM AUTHOR]

Benzene derivatives in wastewater have negative impacts on ecosystems and human health, making their removal prior to discharge imperative. In this study, Fe3O4@AC–NH2@Cu–opa (AC–NH2 = aminoclay, Cu–opa = [Cu(opa)(bipy)0.5(H2O)]n (H2opa = 3-(4-oxypyridinium-1-yl) phthalic acid)) nanoparticles (NPs) were synthesized as adsorbent and catalyst for phenolic compound removal from wastewater. Fe3O4@AC–NH2@Cu–opa NPs demonstrated outstanding performance in the adsorption of phenol, exhibiting a remarkable adsorption capacity of up to 166.39 mg g−1 according to the Langmuir model. The composite also exhibited higher Fenton activity toward the degradation of electron-rich organic phenolic pollutants, with a rate approximately 3.4 times higher than that of Fe3O4 alone. The high catalytic activity of the composite was attributed to the large surface area and abundant active sites of the 2D charge-separated Cu–MOF. Meanwhile, the superparamagnetism of the Fe3O4 core enabled magnetic recollection and reuse without any significant loss of activity. Therefore, use of Fe3O4@AC–NH2@Cu–opa/H2O2 shows potential in an efficient method for the removal of phenolic compounds from wastewater. [ABSTRACT FROM AUTHOR]

Background Portal vein thrombosis (PVT) is a significant issue in cirrhotic patients, necessitating early detection. This study aims to develop a data-driven predictive model for PVT diagnosis in chronic hepatitis liver cirrhosis patients. Methods We employed data from a total of 816 chronic cirrhosis patients with PVT, divided into the Lanzhou cohort (n  = 468) for training and the Jilin cohort (n  = 348) for validation. This dataset encompassed a wide range of variables, including general characteristics, blood parameters, ultrasonography findings and cirrhosis grading. To build our predictive model, we employed a sophisticated stacking approach, which included Support Vector Machine (SVM), Naïve Bayes and Quadratic Discriminant Analysis (QDA). Results In the Lanzhou cohort, SVM and Naïve Bayes classifiers effectively classified PVT cases from non-PVT cases, among the top features of which seven were shared: Portal Velocity (PV), Prothrombin Time (PT), Portal Vein Diameter (PVD), Prothrombin Time Activity (PTA), Activated Partial Thromboplastin Time (APTT), age and Child–Pugh score (CPS). The QDA model, trained based on the seven shared features on the Lanzhou cohort and validated on the Jilin cohort, demonstrated significant differentiation between PVT and non-PVT cases (AUROC = 0.73 and AUROC = 0.86, respectively). Subsequently, comparative analysis showed that our QDA model outperformed several other machine learning methods. Conclusion Our study presents a comprehensive data-driven model for PVT diagnosis in cirrhotic patients, enhancing clinical decision-making. The SVM–Naïve Bayes–QDA model offers a precise approach to managing PVT in this population. [ABSTRACT FROM AUTHOR]

The factors affecting the whitening of solder mask on the surface of printed circuit board assembly (PCBA) after wave soldering and cleaning were studied, and the causes of this phenomenon were analyzed experimentally in the laboratory. The results showed that the ink used to form the solder mask and its thickness had a significant effect on the whitening of PCBA, which could be attributed to the residue of flux in the pores existing in the solder mask itself and being formed between the filler particles and resin matrix during wave soldering at high temperature. This problem can be eliminated by using some dark solder mask inks instead of light ones and decreasing the thickness of solder mask. [ABSTRACT FROM AUTHOR]