Analysis on time delay of tuberculosis among adolescents and young adults in Eastern China.

Background: Tuberculosis (TB) is recognized as a significant global public health concern. Still, there remains a dearth of comprehensive evaluation regarding the specific indicators and their influencing factors of delay for adolescents and young adults. Methods: All notified pulmonary TB (PTB) patients in Jiaxing City were collected between 2005 and 2022 from China's TB Information Management System. Logistic regression models were conducted to ascertain the factors that influenced patient and health system delays for PTB cases, respectively. Furthermore, the impact of the COVID-19 pandemic on local delays has been explored. Results: From January 1, 2005 to December 31, 2022, a total of 5,282 PTB cases were notified in Jiaxing City, including 1,678 adolescents and 3,604 young adults. For patient delay, female (AOR: 1.18, 95%CI: 1.05-1.32), PTB complicated with extra-pulmonary TB (AOR: 1.70, 95% CI: 1.28-2.26), passive case finding (AOR: 1.46, 95% CI: 1.07-1.98) and retreatment (AOR: 1.52, 95% CI: 1.11-2.09) showed a higher risk of delay. For health system delay, minorities (AOR: 0.69, 95% CI: 0.53-0.90) and non-students (AOR: 0.83, 95% CI: 0.71-0.98) experienced a lower delay. Referral (AOR: 1.46, 95% CI: 1.29-1.65) had a higher health system delay compared with clinical consultation. Furthermore, county hospitals (AOR: 1.47, 95% CI: 1.32-1.65) and etiological positive results (AOR: 1.46, 95% CI: 1.30-1.63) were associated with comparatively high odds of patient delay. Contrarily, county hospitals (AOR: 0.88, 95% CI: 0.78-1.00) and etiological positive results (AOR: 0.67, 95% CI: 0.59-0.74) experienced a lower health system delay. Besides, the median of patient delay, health system delay, and total delay during the COVID-19 pandemic were significantly lower than that before. Conclusion: In general, there has been a noteworthy decline in the notification rate of PTB among adolescents and young adults in Jiaxing City while the declining trend was not obvious in patient delay, health system delay, and total delay, respectively. It also found factors such as gender, case-finding method, and the hospital level might influence the times of seeking health care and diagnosis in health agencies. These findings will provide valuable insights for refining preventive and treatment strategies for TB among adolescents and young adults.

SIRT3 acts as a novel biomarker for the diagnosis of lung cancer: A retrospective study.

ABSTRACT: Lung cancer (LC) is a prevalent malignancy worldwide with increased morbidity and mortality. Mitochondrial nicotinamide adenine dinucleotide-dependent deacetylase sirtuin-3 (SIRT3) has been reported to be involved in tumorigenesis. In this retrospective study, we measured the expression and diagnostic value of SIRT3 in LC patients.Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to measure serum SIRT3 mRNA level in 150 LC patients and 52 healthy volunteers. SIRT3 protein level was detected using western blot for 84 pairs of LC and adjacent normal tissues. The association of SIRT3 mRNA level with clinical parameters of LC patients was estimated via chi-square test. Receiver operating characteristic curve (ROC) was plotted to evaluate the diagnostic performance of serum SIRT3 in LC patients.SIRT3 mRNA and protein levels were significantly decreased in LC tissues and serum samples, compared with corresponding controls (P < .05). Moreover, the expression of SIRT3 mRNA was negatively associated with tumor size (P = .002), tumor node metastasis stage (P < .001), and metastasis (P < .001). ROC curve demonstrated that serum SIRT3 could distinguish LC patients from healthy individuals, with an area under the curve of 0.918. The optimal cutoff value was 3.12, reaching a sensitivity of 86.4%, and a specificity of 94%.SIRT3 expression is significantly down-regulated in LC serum and tissues. SIRT3 may be employed as a promising biomarker in the early diagnosis of LC.

Correlation Analysis between the Viral Load and the Progression of COVID-19.

OBJECTIVES: This study is aimed at exploring the relationship of the viral load of coronavirus disease 2019 (COVID-19) with lymphocyte count, neutrophil count, and C-reactive protein (CRP) and investigating the dynamic change of patients' viral load during the conversion from mild COVID-19 to severe COVID-19, so as to clarify the correlation between the viral load and progression of COVID-19. METHODS: This paper included 38 COVID-19 patients admitted to the First Hospital of Jiaxing from January 28, 2020, to March 6, 2020, and they were clinically classified according to the Guidelines on the Novel Coronavirus-Infected Pneumonia Diagnosis and Treatment. According to the instructions of the Nucleic Acid Detection Kit for the 2019 novel coronavirus (SARS-CoV-2), respiratory tract specimens (throat swabs) were collected from patients for nucleic acid testing. Patients' lymphocyte count and neutrophil count were determined by blood routine examination, and CRP was measured by biochemical test. RESULTS: The results of our study suggested that the cycle threshold (Ct) value of Nucleocapsid protein (N) gene examined by nucleic acid test was markedly positively correlated with lymphocyte count (p = 0.0445, R 2 = 0.1203), but negatively correlated with neutrophil count (p = 0.0446, R 2 = 0.1167) and CRP (p = 0.0393, R 2 = 0.1261), which indicated that patients with a higher viral load tended to have lower lymphocyte count but higher neutrophil count and CRP. Additionally, we detected the dynamic change of Ct value in patients who developed into a severe case, finding that viral load of 3 patients increased before disease progression, whereas this phenomenon was not found in 2 patients with underlying diseases. CONCLUSION: The results of this study demonstrated that viral load of SARS-CoV-2 is significantly negatively correlated with lymphocyte count, but markedly positively correlated with neutrophil count and CRP. The rise of viral load is very likely to be the key factor leading to the overloading of the body's immune response and resulting in the disease progression into severe disease.

Unsupervised Anomaly Detection with Distillated Teacher-Student Network Ensemble.

We address the problem of unsupervised anomaly detection for multivariate data. Traditional machine learning based anomaly detection algorithms rely on specific assumptions of normal patterns and fail to model complex feature interactions and relations. Recently, existing deep learning based methods are promising for extracting representations from complex features. These methods train an auxiliary task, e.g., reconstruction and prediction, on normal samples. They further assume that anomalies fail to perform well on the auxiliary task since they are never trained during the model optimization. However, the assumption does not always hold in practice. Deep models may also perform the auxiliary task well on anomalous samples, leading to the failure detection of anomalies. To effectively detect anomalies for multivariate data, this paper introduces a teacher-student distillation based framework Distillated Teacher-Student Network Ensemble (DTSNE). The paradigm of the teacher-student distillation is able to deal with high-dimensional complex features. In addition, an ensemble of student networks provides a better capability to avoid generalizing the auxiliary task performance on anomalous samples. To validate the effectiveness of our model, we conduct extensive experiments on real-world datasets. Experimental results show superior performance of DTSNE over competing methods. Analysis and discussion towards the behavior of our model are also provided in the experiment section.

Positive Stool Test Results Suggest that the Discharge Standard for COVID-19 Needs Improvement.

We report on a hospitalized patient with 2019 novel coronavirus disease whose fecal samples tested negative 22 days after respiratory samples tested negative, highlighting that the duration of viral shedding is longer than that previously expected. Current clinical examinations for treatment and discharge standards are limited to respiratory samples. However, we believe that nucleic acid testing of both respiratory and fecal samples is necessary for discharging patients. Further studies are needed to confirm the potential of fecal-oral transmission or fecal-respiratory transmission via aerosols.

Simulation of Proton-Induced DNA Damage Patterns Using an Improved Clustering Algorithm.

Simulations of deoxyribonucleic acid (DNA) molecular damage use the traversal algorithm that has the disadvantages of being time-consuming, slowly converging, and requiring high-performance computer clusters. This work presents an improved version of the algorithm, "density-based spatial clustering of applications with noise" (DBSCAN), using a KD-tree approach to find neighbors of each point for calculating clustered DNA damage. The resulting algorithm considers the spatial distributions for sites of energy deposition and hydroxyl radical attack, yielding the statistical probability of (single and double) DNA strand breaks. This work achieves high accuracy and high speed at calculating clustered DNA damage that has been induced by proton treatment at the molecular level while running on an i7 quad-core CPU. The simulations focus on the indirect effect generated by hydroxyl radical attack on DNA. The obtained results are consistent with those of other published experiments and simulations. Due to the array of chemical processes triggered by proton treatment, it is possible to predict the effects that different track structures of various energy protons produce on eliciting direct and indirect damage of DNA.

Therapeutic Effects of SRT2104 on Lung Injury in Rats with Emphysema via Reduction of Type II Alveolar Epithelial Cell Senescence.

Chronic obstructive pulmonary disease (COPD) is one of the most prevalent and severe diseases worldwide with high societal and health care costs. The pathogenesis of COPD is very complicated, and no curative treatment is available. Cellular senescence promotes the development of COPD. Type II alveolar epithelial cells (AECII) play a momentous role in lung tissue repair and maintenance of alveolar homeostasis. Sirtuin 1 (SIRT1), an antiaging molecule involved in the response to chronic inflammation and oxidative stress, regulates many pathophysiological changes including stress resistance, apoptosis, inflammation, and cellular senescence. This study aimed to investigate whether the pharmacological SIRT1 activator SRT2104 protects against AECII senescence in rats with emphysema. Our findings confirmed that SRT2104 administration reduced the pathological characteristics of emphysema and improved lung function parameters, including pulmonary resistance, pulmonary dynamic compliance, and peak expiratory flow. Moreover, SRT2104 treatment upregulated the expression of surfactant proteins A and C, SIRT1, and forkhead box O 3a (FoxO3a), decreased senescence-associated-ß-galactosidase (SA-ß-gal) activity, increased SIRT1 deacetylase activity, and downregulated the levels of p53 and p21. Therefore, SRT2104 administration protected against AECII senescence in rats with emphysema via SIRT1/FoxO3a and SIRT1/p53 signaling pathways and may provide a novel potential therapeutic strategy for COPD.

[Study of clustered damage in DNA after proton irradiation based on density-based spatial clustering of applications with noise algorithm].

The deoxyribonucleic acid (DNA) molecule damage simulations with an atom level geometric model use the traversal algorithm that has the disadvantages of quite time-consuming, slow convergence and high-performance computer requirement. Therefore, this work presents a density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm based on the spatial distributions of energy depositions and hydroxyl radicals (·OH). The algorithm with probability and statistics can quickly get the DNA strand break yields and help to study the variation pattern of the clustered DNA damage. Firstly, we simulated the transportation of protons and secondary particles through the nucleus, as well as the ionization and excitation of water molecules by using Geant4-DNA that is the Monte Carlo simulation toolkit for radiobiology, and got the distributions of energy depositions and hydroxyl radicals. Then we used the damage probability functions to get the spatial distribution dataset of DNA damage points in a simplified geometric model. The DBSCAN clustering algorithm based on damage points density was used to determine the single-strand break (SSB) yield and double-strand break (DSB) yield. Finally, we analyzed the DNA strand break yield variation trend with particle linear energy transfer (LET) and summarized the variation pattern of damage clusters. The simulation results show that the new algorithm has a faster simulation speed than the traversal algorithm and a good precision result. The simulation results have consistency when compared to other experiments and simulations. This work achieves more precise information on clustered DNA damage induced by proton radiation at the molecular level with high speed, so that it provides an essential and powerful research method for the study of radiation biological damage mechanism.