Explore the vast range of titles available.

We assembled the sequences from deep RNA sequencing experiments by the Genotype-Tissue Expression (GTEx) project, to create a new catalog of human genes and transcripts, called CHESS. The new database contains 42,611 genes, of which 20,352 are potentially protein-coding and 22,259 are noncoding, and a total of 323,258 transcripts. These include 224 novel protein-coding genes and 116,156 novel transcripts. We detected over 30 million additional transcripts at more than 650,000 genomic loci, nearly all of which are likely nonfunctional, revealing a heretofore unappreciated amount of transcriptional noise in human cells. The CHESS database is available at http://ccb.jhu.edu/chess .

In recent years, microfluidic lab-on-paper devices have emerged as a rapid and low-cost alternative to traditional laboratory tests. Additionally, they were widely considered as a promising solution for point-of-care testing (POCT) at home or regions that lack medical infrastructure and resources. This review describes important advances in microfluidic lab-on-paper diagnostics for human health monitoring and disease diagnosis over the past five years. The review commenced by explaining the choice of paper, fabrication methods, and detection techniques to realize microfluidic lab-on-paper devices. Then, the sample pretreatment procedure used to improve the detection performance of lab-on-paper devices was introduced. Furthermore, an in-depth review of lab-on-paper devices for disease measurement based on an analysis of urine samples was presented. The review concludes with the potential challenges that the future development of commercial microfluidic lab-on-paper platforms for human disease detection would face.

Human activity recognition without equipment plays a vital role in smart home applications, freeing humans from the shackles of wearable devices. In this paper, by using the channel state information (CSI) of the WiFi signal, semi-supervised transfer learning with dynamic associate domain adaptation is proposed for human activity recognition. In order to improve the CSI quality and denoising of CSI, we carried out missing packet filling, burst noise removal, background estimation, feature extraction, feature enhancement, and data augmentation in the data pre-processing stage. This paper considers the problem of environment-independent human activity recognition, also known as domain adaptation. The pre-trained model is trained from the source domain by collecting a complete labeled dataset of all of the CSI of human activity patterns. Then, the pre-trained model is transferred to the target environment through the semi-supervised transfer learning stage. Therefore, when humans move to different target domains, a partial labeled dataset of the target domain is required for fine-tuning. In this paper, we propose a dynamic associate domain adaptation called DADA. By modifying the existing associate domain adaptation algorithm, the target domain can provide a dynamic ratio of labeled dataset/unlabeled dataset, while the existing associate domain adaptation algorithm only allows target domains with the unlabeled dataset. The advantage of DADA is that it provides a dynamic strategy to eliminate different effects on different environments. In addition, we further designed an attention-based DenseNet model, or AD, as our training network, which is modified by an existing DenseNet by adding the attention function. The solution we proposed was simplified to DADA-AD throughout the paper. The experimental results show that for domain adaptation in different domains, the accuracy of human activity recognition of the DADA-AD scheme is 97.4%. It also shows that DADA-AD has advantages over existing semi-supervised learning schemes.

In addition to increased morbidity and mortality caused by respiratory and cardiovascular diseases, air pollution may also negatively affect the brain and contribute to central nervous system diseases. Air pollution is a mixture comprised of several components, of which ultrafine particulate matter (UFPM; <100 nm) is of much concern, as these particles can enter the circulation and distribute to most organs, including the brain. A major constituent of ambient UFPM is represented by traffic-related air pollution, mostly ascribed to diesel exhaust (DE). Human epidemiological studies and controlled animal studies have shown that exposure to air pollution may lead to neurotoxicity. In addition to a variety of behavioral abnormalities, two prominent effects caused by air pollution are oxidative stress and neuroinflammation, which are seen in both humans and animals and are confirmed by in vitro studies. Among factors which can affect neurotoxic outcomes, age is considered the most relevant. Human and animal studies suggest that air pollution (and DE) may cause developmental neurotoxicity and may contribute to the etiology of neurodevelopmental disorders, including autistic spectrum disorders. In addition, air pollution exposure has been associated with increased expression of markers of neurodegenerative disease pathologies.

Background Escalating prevalence of autism spectrum disorders (ASD) in recent decades has triggered increasing efforts in understanding roles played by environmental risk factors as a way to address this widespread public health concern. Several epidemiological studies show associations between developmental exposure to traffic-related air pollution and increased ASD risk. In rodent models, a limited number of studies have shown that developmental exposure to ambient ultrafine particulates or diesel exhaust (DE) can result in behavioral phenotypes consistent with mild ASD. We performed a series of experiments to determine whether developmental DE exposure induces ASD-related behaviors in mice. Results C57Bl/6J mice were exposed from embryonic day 0 to postnatal day 21 to 250–300 μg/m 3 DE or filtered air (FA) as control. Mice exposed developmentally to DE exhibited deficits in all three of the hallmark categories of ASD behavior: reduced social interaction in the reciprocal interaction and social preference tests, increased repetitive behavior in the T-maze and marble-burying test, and reduced or altered communication as assessed by measuring isolation-induced ultrasonic vocalizations and responses to social odors. Conclusions These findings demonstrate that exposure to traffic-related air pollution, in particular that associated with diesel-fuel combustion, can cause ASD-related behavioral changes in mice, and raise concern about air pollution as a contributor to the onset of ASD in humans.

During the Nationalist period (1928–1949) in China, the notion that China’s territory mirrors the shape of a begonia or a mulberry leaf gained wide recognition. This analogy ingrained itself into public perceptions of modern China’s boundaries and was often assumed without question. As foreign forces—symbolized by silk-worms—encroached upon the leaf-like territory, the leaf trope emerged as a platform for various patriotic appeals during wartime. This research explores the evolution of the leaf trope for China’s territory in the 1930s, probing the historical and cultural connotations embedded in it. The discussion expands to incorporate intellectual resources associated with the Song-era military commander Yue Fei and the leaf trope, as they jointly influenced the portrayal of China’s territory across textual and visual mediums. In this light, territorial conceptualizations in modern China were shaped by ideological constructs envisioning a future rooted in the past.

Background Proton therapy, with its superior dose distribution, has emerged as a promising radiotherapy (RT) technique. This study evaluates the clinical benefits of proton therapy in reducing treatment-related toxicities in breast cancer patients undergoing radiotherapy. Methods A retrospective analysis was performed using the Chang Gung Research Database, identifying 8189 breast cancer patients treated with adjuvant radiotherapy from 2012 to 2022. Patients received either intensity-modulated proton therapy (IMPT, n  = 501) or photon-based IMRT/VMAT ( n  = 7688). Propensity score weighting was applied to adjust for baseline differences. The clinical outcomes photon and proton therapy were investigated, with a particular focus on radiation-induced toxicities including dermatitis, pulmonary changes, and cardiac events. In addition, N-terminal pro-brain natriuretic peptide (NT-proBNP), a biomarker of cardiac stress, was prospectively assessed in a subset of patients to assess subclinical cardiac injury between 2023 and 2024. Results Compared with photon therapy, proton therapy significantly reduced the incidence of radiographic pulmonary changes and subclinical cardiac biomarker elevations, while maintaining comparable loco-regional control and overall survival. Proton therapy significantly reduced the incidence of radiographic pulmonary changes (5.8% vs. 10.0%, p  = 0.02; HR: 0.64, 95% CI 0.44–0.93) and grade 2 radiation dermatitis (13.4% vs. 20.9%, p  < 0.001), though grade ≥ 1 skin toxicity was more common with IMPT (82% vs. 57.1%). Among left-sided patients, photon RT resulted in significantly greater NT-proBNP elevation post-treatment compared to proton therapy (mean ratio: 1.16 vs. 0.95, p  = 0.0099), suggesting more subclinical cardiac stress. However, no significant difference in clinically evident cardiac events was noted between the groups during the follow-up period. Conclusions Proton therapy is associated with reduced radiation-induced pulmonary toxicity and moderate dermatitis, while achieving comparable oncologic outcomes to photon therapy. The lower NT-proBNP elevation suggests potential cardiac sparing, although longer follow-up is needed to confirm clinically meaningful cardiac benefits. These findings highlight the potential role of proton therapy in mitigating treatment-related toxicities in breast cancer patients at elevated risk for cardiopulmonary complications.

Buddleja davidii Franch. [Scrophulariaceae] commonly found in the mountainous regions of Taiwan, is used as herbal medicine around the world. In spite of this , no research has been conducted on the physiological activities of B. davidii extracts from different parts of the plant and from plants grown at different altitudes. In this study, B . davidii flower, stem, and leaf extracts were prepared using distilled water, methanol, and 60% ethanol as extraction solvents. The total phenolic content of the extracts served as an indicator of their activities. Our results indicated high bioactivity in the water extract of B . davidii flowers grown at 1,500 m, the 60% ethanol extract of B . davidii stems grown at 1,000 m, and the methanol extract of B . davidii leaves grown at 1,500 m. The freeze-dried leaf extract exhibited the highest antioxidant activity, which may be attributed to its abundance of phenylethanoid glycosides and flavonoids. The major bioactive components of the flower extract were crocin, crocetin, quercetin, and rutin. Those in the stem extract were luteolin, naringenin, quercetin, acacetin, and apigenin; and in the leaf extract were verbascoside, isoverbascoside and oleanolic acid. These compounds were potentially responsible for the antiaging and anti-inflammatory activity of the flower extract (IC 50 : 28.6–125.1 mg/L), the antibacterial activity of the stem extract (minimum inhibitory concentration: 60–100 mg/L), and the antityrosinase activity of the leaf extract (IC 50 : 38.17 mg/L). For example, the antiaging activity of B. davidii flower extract was found to be superior to or comparable with that of the positive controls, which include EGCG (IC 50 : 67.2–162.8 mg/L), 1,10-phenanthroline (IC 50 : 46.7 mg/L), gallic acid (IC 50 : 132.6 mg/L), and tannic acid (IC 50 : 140.3 mg/L). Moreover, these extracts can be deemed safe, as they demonstrated no toxic effects on CCD-966SK, HEMn, and RAW264.7 cells at a concentration of 200 mg/L. To our knowledge, this is the first report revealing differences in activities of B. davidii extracts based on plant part and altitudes. The findings provide insights for potential applications of the identified bioactive compounds in health foods, herbal medicines, and cosmetics.

This study aimed to evaluate the effect of Astragalus polysaccharides (PG2) on reducing chemotherapy-induced fatigue (CIF) and toxicity, thereby encouraging compliance to chemotherapy. This was a randomized, placebo-controlled, phase 2 study. Patients with stage II/III early breast cancer planning to undergo adjuvant anthracycline-based chemotherapy were randomly assigned to receive PG2 500 mg or placebo on days 1, 3, and 8 every 21 days. The fatigue global score (FGS) was assessed using the brief fatigue inventory (BFI)-Taiwan. The Breast Cancer-Specific Module of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaires-Core30 evaluated the health-related quality of life during the first four cycles of adjuvant chemotherapy. Overall, 66 eligible patients were equally randomized into the PG2 and placebo groups between March 01, 2018, and March 09, 2021. The mean change in the FGS and fatigue intensity did not significantly differ between both groups. However, the FGS and fatigue intensity were less aggravated in the first four cycles in the premenopausal-PG2 group than in the placebo group. Our study concluded PG2 combined with adjuvant chemotherapy can reduce CIF, insomnia, the negative effect on future perspectives, and improve global health status, especially for premenopausal patients with breast cancer. Trial registration number: NCT03314805 registered on 19/10/2017.

A novel assay platform consisting of a microfluidic sliding double-track paper-based chip and a hand-held Raspberry Pi detection system is proposed for determining the albumin-to-creatine ratio (ACR) in human urine. It is a clinically important parameter and can be used for the early detection of related diseases, such as renal insufficiency. In the proposed method, the sliding layer of the microchip is applied and the sample diffuses through two parallel filtration channels to the reaction/detection areas of the microchip to complete the detection reaction, which is a simple method well suited for self-diagnosis of ACR index in human urine. The RGB (red, green, and blue) value intensity signals of the reaction complexes in these two reaction zones are analyzed by a Raspberry Pi computer to derive the ACR value (ALB and CRE concentrations). It is shown that the G + B value intensity signal is linearly related to the ALB and CRE concentrations with the correlation coefficients of R2 = 0.9919 and R2 = 0.9923, respectively. It is additionally shown that the ALB and CRE concentration results determined using the proposed method for 23 urine samples were collected from real suffering chronic kidney disease (CKD) patients are in fine agreement with those acquired operating a traditional high-reliability macroscale method. Overall, for point-of-care (POC) CKD diagnosis and monitoring in clinical applications, the results prove that the proposed method offers a convenient, real time, reliable, and low-spending solution for POC CKD diagnosis.