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Objective·To explore the characteristics of sensory gating and its variation in children with first episode schizophrenia (COS) by using a new technique of prepulse suppression (PI).Methods·By using the ERP recording and analysis system of brain products, PI was detected in 56 patients with COS and 38 healthy children (NC) using the paradigm of single strong stimulus and weak stimulus+strong stimulus. The patients′ performance was comprehensively evaluated with Positive and Negative Syndrome Scale (PANSS), Social Disability Screening Schedule (SDSS), Social Adjustment Rating Scale (SSRS), and Family Interview Schedule (FIS).Results·The social objective support formed by summing up the above scales was compared with the quantitative stanard of social support [the standard of social support scale was (8±2) points, and the value of COS group was (10±3) points], and the difference was statistically significant (P=0.007). No correlation was found between PI and PANSS total score and each factor score (all P>0.05). The latency of startle reflex in the COS group was longer than that in the NC group [the NC group was (86±11) ms, the COS group was (97±13) ms, P=0.001]. In the COS group, the amplitude of startle reflex of weak stimulus+strong stimulus was higher, and the latency was longer than that of the NC group [NC group: (39±12) μV, COS group (47±21) μV, P=0.007; the latency of the normal group was (84±17) ms, and that of the COS group was (97±20) ms, P=0.003]. PI inhibition rate in the COS group was lower than that in the NC group [(66±32) % in the NC group, (43±37) % in the COS group, P=0.000].Conclusion·COS patients have the same PI abnormality as adult schizophrenia. The change of PI inhibition may be the result of biological markers reflecting the change of agitated emotion in COS patients.

Chlorella, a single‐celled green algae rich in proteins, holds promise as a source of bioactive peptides. This research aimed to isolate an antioxidant peptide from Chlorella, examining its stability through digestion. Using Response Surface Methodology (RSM), we identified optimal hydrolysis conditions for Chlorella Protein Hydrolysate (CPH): 55°C, pH 8.75, and an enzyme‐to‐substrate ratio of 2.15%. These conditions led to CPH with notable radical scavenging activity against 1,1‐diphenyl‐2‐picrylhydrazyl (DPPH) (53.19 ± 0.99%), 2,2′‐azino‐bis (3‐ethylbenzothiazoline‐6‐sulfonic acid) (ABTS) (76.86 ± 0.28%), hydroxyl radical (53.39 ± 1.74%), and Fe2+ chelating rates of 22.56 ± 2.97%. Subsequently, peptides under 3 kDa isolated by ultrafication proved particularly stable and antioxidative after in vitro digestion. In addition, further purify to obtain component C1 with DPPH radical scavenging ability of 59.62 ± 1.29% and an Fe2+ chelating ability of 49.78 ± 1.55%. Subsequent LC‐MS/MS identification and virtual screening led to the discovery of a stable and antioxidative peptide, Ser‐Gly‐His‐His‐Lys‐Pro‐Leu (SGHHKPL). The study provides the theoretical basis for the development and utilization of chlorella. [ABSTRACT FROM AUTHOR]

A high-sensitivity pH sensor based on an AlGaN/GaN high-electron mobility transistor (HEMT) with a 10 nm thick Au-gated sensing membrane was investigated. The Au nanolayer as a sensing membrane was deposited by electron-beam evaporation and patterned onto the GaN cap layer, which provides more surface-active sites and a more robust adsorption capacity for hydrogen ions (H+) and hydroxide ions (OH−) and thus the sensitivity of the sensor can be significantly enhanced. A quasi-reference electrode was used to minimize the sensing system for the measurement of the microliter solution. The measurement and analysis results demonstrate that the fabricated sensor exhibits a high potential sensitivity of 58.59 mV/pH, which is very close to the Nernstian limit. The current sensitivity is as high as 372.37 μA/pH in the pH range from 4.0 to 9.18, under a 3.5 V drain-source voltage and a 0 V reference-source voltage. Comparison experiments show that the current sensitivity of the Au-gated sensor can reach 3.9 times that of the SiO2-gated sensor. Dynamic titration experiments reveal the pH sensor's ability to promptly respond to immediate pH variations. These findings indicate that this pH sensor can meet most application requirements for advanced medical and chemical analysis. [ABSTRACT FROM AUTHOR]

Depression is increasingly impacting individuals both physically and psychologically worldwide. It has become a global major public health problem and attracts attention from various research fields. Traditionally, the diagnosis of depression is formulated through semi-structured interviews and supplementary questionnaires, which makes the diagnosis heavily relying on physicians’ experience and is subject to bias. However, since the pathogenic mechanism of depression is still under investigation, it is difficult for physicians to diagnose and treat, especially in the early clinical stage. As smart devices and artificial intelligence advance rapidly, understanding how depression associates with daily behaviors can be beneficial for the early stage depression diagnosis, which reduces labor costs and the likelihood of clinical mistakes as well as physicians bias. Furthermore, mental health monitoring and cloud-based remote diagnosis can be implemented through an automated depression diagnosis system. In this article, we propose an attention-based multimodality speech and text representation for depression prediction. Our model is trained to estimate the depression severity of participants using the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) dataset. For the audio modality, we use the collaborative voice analysis repository (COVAREP) features provided by the dataset and employ a Bidirectional Long Short-Term Memory Network (Bi-LSTM) followed by a Time-distributed Convolutional Neural Network (T-CNN). For the text modality, we use global vectors for word representation (GloVe) to perform word embeddings and the embeddings are fed into the Bi-LSTM network. Results show that both audio and text models perform well on the depression severity estimation task, with best sequence level $F_{1}$ F 1 score of 0.9870 and patient-level $F_{1}$ F 1 score of 0.9074 for the audio model over five classes (healthy, mild, moderate, moderately severe, and severe), as well as sequence level $F_{1}$ F 1 score of 0.9709 and patient-level $F_{1}$ F 1 score of 0.9245 for the text model over five classes. Results are similar for the multimodality fused model, with the highest $F_{1}$ F 1 score of 0.9580 on the patient-level depression detection task over five classes. Experiments show statistically significant improvements over previous works. [ABSTRACT FROM AUTHOR]

Purpose: To study the association of apolipoprotein E (APOE) polymorphisms and primary open-angle glaucoma (POAG)., Methods: After a systematic literature search, all relevant studies evaluating the association between APOE polymorphisms and POAG were included. All statistical tests were calculated with Stata 11.0., Results: Twelve independent studies on the APOE gene (1,971 cases, 1,756 controls) and POAG were included. A significant association between the APOE gene and POAG was found in the genetic model of ε4/ε4 versus ε3/ε3 (odds ratio [OR] = 2.09, 95% confidence interval [CI] = 1.12-3.88, p = 0.02). However, no association was detected in the models of ε2/ε2 versus ε3/ε3, ε2/ε3 versus ε3/ε3, ε2/ε4 versus ε3/ε3, ε3/ε4 versus ε3/ε3, allele ε2 versus allele ε3, and allele ε4 versus allele ε3. Subgroup analyses showed that a statistically significant association between the APOE gene and the risk of POAG existed in the genetic model of ε4/ε4 versus ε3/ε3 in Asians (OR = 3.55, 95% CI = 1.06-11.87, p = 0.04). No association was identified between the APOE gene and the risk of POAG in Caucasians., Conclusions: The present meta-analysis indicated that the ε4/ε4 genotype is associated with increased risk of POAG in Asians.

Background: Internet addiction can seriously affect the social functioning and studies of college students in China but measures for addressing this problem have not yet been developed or tested., Objective: Assess the personality characteristics of college students with internet addiction., Methods: Two self-report scales, the Tridimensional Personality Questionnaire (TPQ) and the Chen Internet Addiction Scale (CIAS), were administered to a stratified random sample of 697 college students from colleges and vocational schools in Wenzhou, China. The characteristics of 48 subjects who meet Chen's criteria for internet addiction (score of 64 or greater out of 100 on the CIAS) were compared to those of 649 subjects who did not meet criteria for internet addiction., Results: The prevalence of internet addiction in the sample was 6.9% (95% CI=5.1-9.1%). Compared to students without internet addiction, those with internet addiction were more likely to be male, of Han ethnicity, to have a history of substance use (primarily tobacco and alcohol), and to be a student at a technical college. Students with internet addiction had higher mean (sd) scores on the novelty-seeking subscale of the TPQ [17.9 (1.2) v. 13.0 (1.6), t=16.75 p<0.001] and on the harm-avoidance subscale [17.2 (1.9) v. 14.6 (1.1), t=15.14, p<0.001] but lower scores on the reward-dependence subscale [14.6 (1.4) v. 18.3 (1.7), t=-7.64, p<0.001]. Logistic regression found that the most important independent predictors of internet addiction were Han ethnicity (OR= 5.52, 95% CI=2.00-15.32), male gender (4.40, 1.97-9.81), and substance use (1.08, 1.02-1.15). After adjustment for other variables personality measures were not significantly associated with internet addiction., Conclusion: The prevalence of internet addiction among college students in Wenzhou is similar to that in other parts of China. Significant differences in the personality characteristics assessed by the TPQ between university students with and without internet addiction become non-significant after controlling for gender, ethnicity and substance use patterns.