Multidimensional progressive single-cell sequencing reveals cell microenvironment composition and cancer heterogeneity in lung cancer.

Despite substantial advances in cancer biology and treatment, the clinical outcomes of patients with lung cancer remain unsatisfactory. The tumor microenvironment (TME) is a potential target. Using single-cell RNA sequencing, we could distinguish eight distinct cell types in the lung cancer microenvironment, demonstrating substantial intratumoral heterogeneity in 19 different lung cancer tumor samples. Through the re-dimensional grouping of cancer-associated fibroblasts (CAFs), myeloid cells, epithelial cells, natural killer (NK) cells, and T cells, the difference in the TME of lung cancer was revealed. We discovered SFTPB, SFN, and KRT8 as possible predictive biomarkers for lung cancer by assessing the gene expression patterns in epithelial cells. Examining cell-to-cell communications showed a robust association between the quantity of matrix CAFs, epithelial cells, and macrophages in the thrombospondin signaling pathway. Additionally, we found that the amyloid precursor protein signaling pathway primarily originated from the matrix, and inflammatory cancer-associated endothelial and fibroblast cells showed a co-expression relationship with myeloid cells and B cells. Through cell-to-cell correlation analysis, we found positive regulation between NK cells, regulatory T cells, GZMB-CD8 T cells, and GZMK-CD8 T cells, which could play a role in developing immune TMEs. These findings support studies on cancer heterogeneity and add to our understanding of lung cancer's cellular microenvironment.

Application Effectiveness of Segment IV Portal Vein Reconstruction for Early Postoperative Liver Function Recovery in Split Liver Transplantation.

The objective of this study was to investigate the significance of portal vein reconstruction in segment IV of the liver on early postoperative liver function recovery in split liver transplantation. The clinical data of patients of right trilobe split liver transplantation in our center were analyzed and divided into two groups, including a group without portal vein reconstruction and a group with portal vein reconstruction. Clinical data of alanine aminotransferase (ALT), aspartate transaminase (AST), albumin (ALB), creatinine (Cr), total bilirubin (TB), alkaline phosphatase (ALP), gamma-glutamyl Transferase (GGT), lactic acid (Lac), and international normalized ratio (INR) levels were analyzed. The technique of segment IV portal vein reconstruction is beneficial to the early postoperative recovery of liver function. Statistically, there was no significant effect of portal vein reconstruction in the IV segment of the liver on the recovery of liver function within 1 week after split liver transplantation. There was no significant difference in survival rate between the control group and reconstruction group over the 6 months follow-up period after surgery.

Recent Development in Laminar Transition Metal Dichalcogenides-Based Membranes Towards Water Desalination: A Review.

Transition metal dichalcogenides (TMDCs)-based laminar membranes have gained significant interest in energy storage, fuel cell, gas separation, wastewater treatment, and desalination applications due to single layer structure, good functionality, high mechanical strength, and chemical resistivity. Herein, we review the recent efforts and development on TMDCs-based laminar membranes, and focus is given on their fabrication strategies. Further, TMDCs-based laminar membranes for water purification and seawater desalination are discussed in detail. Finally, present their merits, limits and future challenges needed in this area.

Lightweight Internet of Things Botnet Detection Using One-Class Classification.

Like smart phones, the recent years have seen an increased usage of internet of things (IoT) technology. IoT devices, being resource constrained due to smaller size, are vulnerable to various security threats. Recently, many distributed denial of service (DDoS) attacks generated with the help of IoT botnets affected the services of many websites. The destructive botnets need to be detected at the early stage of infection. Machine-learning models can be utilized for early detection of botnets. This paper proposes one-class classifier-based machine-learning solution for the detection of IoT botnets in a heterogeneous environment. The proposed one-class classifier, which is based on one-class KNN, can detect the IoT botnets at the early stage with high accuracy. The proposed machine-learning-based model is a lightweight solution that works by selecting the best features leveraging well-known filter and wrapper methods for feature selection. The proposed strategy is evaluated over different datasets collected from varying network scenarios. The experimental results reveal that the proposed technique shows improved performance, consistent across three different datasets used for evaluation.

Hydrazine Detection during Ammonia Electro-oxidation Using an Aggregation-Induced Emission Dye.

Ammonia electro-oxidation is an extremely significant reaction with regards to the nitrogen cycle, hydrogen economy, and wastewater remediation. The design of efficient electrocatalysts for use in the ammonia electro-oxidation reaction (AOR) requires comprehensive understanding of the mechanism and intermediates involved. In this study, aggregation-induced emission (AIE), a robust fluorescence sensing platform, is employed for the sensitive and qualitative detection of hydrazine (N2H4), one of the important intermediates during the AOR. Here, we successfully identified N2H4 as a main intermediate during the AOR on the model Pt/C electrocatalyst using 4-(1,2,2-triphenylvinyl)benzaldehyde (TPE-CHO), an aggregation-induced emission luminogen (AIEgen). We propose the AOR mechanism for Pt with N2H4 being formed during the dimerization process (NH2 coupling) within the framework of the Gerischer and Mauerer mechanism. The unique chemodosimeter approach demonstrated in this study opens a novel pathway for understanding electrochemical reactions in depth.

A Region-Based Deep Level Set Formulation for Vertebral Bone Segmentation of Osteoporotic Fractures.

Accurate segmentation of the vertebrae from medical images plays an important role in computer-aided diagnoses (CADs). It provides an initial and early diagnosis of various vertebral abnormalities to doctors and radiologists. Vertebrae segmentation is very important but difficult task in medical imaging due to low-contrast imaging and noise. It becomes more challenging when dealing with fractured (osteoporotic) cases. This work is dedicated to address the challenging problem of vertebra segmentation. In the past, various segmentation techniques of vertebrae have been proposed. Recently, deep learning techniques have been introduced in biomedical image processing for segmentation and characterization of several abnormalities. These techniques are becoming popular for segmentation purposes due to their robustness and accuracy. In this paper, we present a novel combination of traditional region-based level set with deep learning framework in order to predict shape of vertebral bones accurately; thus, it would be able to handle the fractured cases efficiently. We termed this novel Framework as "FU-Net" which is a powerful and practical framework to handle fractured vertebrae segmentation efficiently. The proposed method was successfully evaluated on two different challenging datasets: (1) 20 CT scans, 15 healthy cases, and 5 fractured cases provided at spine segmentation challenge CSI 2014; (2) 25 CT image data (both healthy and fractured cases) provided at spine segmentation challenge CSI 2016 or xVertSeg.v1 challenge. We have achieved promising results on our proposed technique especially on fractured cases. Dice score was found to be 96.4 ± 0.8% without fractured cases and 92.8 ± 1.9% with fractured cases in CSI 2014 dataset (lumber and thoracic). Similarly, dice score was 95.2 ± 1.9% on 15 CT dataset (with given ground truths) and 95.4 ± 2.1% on total 25 CT dataset for CSI 2016 datasets (with 10 annotated CT datasets). The proposed technique outperformed other state-of-the-art techniques and handled the fractured cases for the first time efficiently.

Canadian expert consensus: management of hypersensitivity reactions to intravenous iron in adults.

BACKGROUND AND OBJECTIVES: Rare but potentially life-threatening hypersensitivity reactions can occur during the administration of intravenous iron. To provide guidance to healthcare professionals caring for adults receiving intravenous iron, a panel of 10 Canadian clinical experts developed a practical algorithm for the identification and management of hypersensitivity reactions to intravenous iron. MATERIALS AND METHODS: A systematic search of PubMed to February 2018 was performed. Articles related to hypersensitivity reactions were selected for review. The algorithm was developed during a 1-day live meeting based on the literature review and clinical expertise where evidence was lacking. The algorithm was then refined through an iterative process involving a web-based platform and virtual meetings. RESULTS: The algorithm provides guidance to healthcare professionals in preparing for and administering IV iron, as well as recognizing and managing hypersensitivity reactions to intravenous iron. Considerations for re-challenging patients who have experienced prior reactions are provided. CONCLUSION: Healthcare professionals who are involved in the care of patients receiving intravenous iron should be trained to anticipate, recognize and manage hypersensitivity reactions to intravenous iron to optimize patient care.

Development, administration, and validity evidence of a subspecialty preparatory test toward licensure: a pilot study.

BACKGROUND: Trainees in medical subspecialties lack validated assessment scores that can be used to prepare for their licensing examination. This paper presents the development, administration, and validity evidence of a constructed-response preparatory test (CRPT) administered to meet the needs of nephrology trainees. METHODS: Learning objectives from the licensing examination were used to develop a test blueprint for the preparatory test. Messick's unified validity framework was used to gather validity evidence for content, response process, internal structure, relations to other variables, and consequences. Questionnaires were used to gather data on the trainees' perception of examination preparedness, item clarity, and curriculum adequacy. RESULTS: There were 10 trainees and 5 faculty volunteers who took the test. The majority of trainees passed the constructed-response preparatory test. However, many scored poorly on items assessing renal pathology and physiology knowledge. We gathered the following five sources of validity evidence: (1) Content: CRPT mapped to the licensing examination blueprint, with items demonstrating clarity and range of difficulty; (2) Response process: moderate rater agreement (intraclass correlation = .58); (3) Internal structure: sufficient reliability based on generalizability theory (G-coefficient = .76 and Φ-coefficient = .53); (4) Relations to other variables: CRPT scores reflected years of exposure in nephrology and clinical practice; (5) Consequences: post-assessment survey revealed that none of the test takers felt "poorly prepared" for the upcoming summative examination and that their studying would increase in duration and be adapted in terms of content focus. CONCLUSIONS: Preparatory tests using constructed response items mapped to licensure examination blueprint can be developed and used at local program settings to help prepare learners for subspecialty licensure examinations. The CRPT and questionnaire data identified shortcomings of the nephrology training program curriculum. Following the preparatory test, trainees expressed an improved sense of preparedness for their licensing examination.

Atypical antipsychotic drugs and the risk for acute kidney injury and other adverse outcomes in older adults: a population-based cohort study.

BACKGROUND: Several adverse outcomes attributed to atypical antipsychotic drugs, specifically quetiapine, risperidone, and olanzapine, are known to cause acute kidney injury (AKI). Such outcomes include hypotension, acute urinary retention, and the neuroleptic malignant syndrome or rhabdomyolysis. OBJECTIVE: To investigate the risk for AKI and other adverse outcomes associated with use of atypical antipsychotic drugs versus nonuse. DESIGN: Population-based cohort study. SETTING: Ontario, Canada, from 2003 to 2012. PATIENTS: Adults aged 65 years or older who received a new outpatient prescription for an oral atypical antipsychotic drug (n=97,777) matched 1:1 with those who did not receive such a prescription. MEASUREMENTS: The primary outcome was hospitalization with AKI (assessed by using a hospital diagnosis code and, in a subpopulation, serum creatinine levels) within 90 days of prescription for atypical antipsychotic drugs. RESULTS: Atypical antipsychotic drug use versus nonuse was associated with a higher risk for hospitalization with AKI (relative risk [RR], 1.73 [95% CI, 1.55 to 1.92]). This association was consistent when AKI was assessed in a subpopulation for which information on serum creatinine levels was available (5.46% vs. 3.34%; RR, 1.70 [CI, 1.22 to 2.38]; absolute risk increase, 2.12% [CI, 0.80% to 3.43%]). Drug use was also associated with hypotension (RR, 1.91 [CI, 1.60 to 2.28]), acute urinary retention (RR, 1.98 [CI, 1.63 to 2.40]), and all-cause mortality (RR, 2.39 [CI, 2.28 to 2.50]). LIMITATION: Only older adults were included in the study. CONCLUSION: Atypical antipsychotic drug use is associated with an increased risk for AKI and other adverse outcomes that may explain the observed association with AKI. The findings support current safety concerns about the use of these drugs in older adults. PRIMARY FUNDING SOURCE: Academic Medical Organization of Southwestern Ontario.

Preoperative Cylex assay predicts rejection risk in patients with kidney transplant.

INTRODUCTION AND OBJECTIVES: The ImmuKnow assay measures cell-mediated immunity by quantifying ATP release from CD4+ T-cells in peripheral blood. Herein, we hypothesized that this assay could predict complications associated with over-/under-immunosuppression in patients with kidney transplant (KT). METHODS: Sixty-seven patients undergoing KT were recruited prospectively and had ATP levels measured preoperatively, and at specified intervals over two months. Clinicians were blinded to ATP levels. Clinical events including rejection and infection/cancer were documented with a median follow-up of 21 months. Parameters including absolute ATP levels and changes in ATP patterns (slopes, delta) were analyzed. Association between ATP parameters and clinical outcomes was compared using the likelihood-ratio test and Kaplan-Meier curves. RESULTS: Absolute ATP values postoperatively had poor predictive value with regard to rejection or infection/malignancy. As well, changes in ATP values were poorly associated with complications. Importantly, patients with pre-transplant ATP values <300 ng/mL had significantly less rejection episodes vs. those with ATP values >300 ng/mL (p < 0.0001). CONCLUSIONS: For the first time, we have evidence that a preoperative ImmuKnow level can stratify patients with KT into low/high risk groups for rejection. Future studies used to assess the utility of this assay to design individualized immunosuppressive regimens are required.

Non-surgical interventions to control bleeding from arteriovenous fistulas and grafts inside and outside the hemodialysis unit: a scoping review.

Background: Prolonged bleeding from arteriovenous fistulas (AVF) and arteriovenous grafts (AVG) associates with worse outcomes; Within the hemodialysis unit these outcomes include anemia and quality of life disruptions, and outside the hemodialysis unit includes fatal hemorrhage. However, various guidelines for AVF/AVG bleeding management inside and outside the hemodialysis unit lack consensus. Methods: A scoping review was conducted of four databases, from inception to 17 February 2024. The study population was hemodialysis patients experiencing bleeding from AVF or AVG. Studies that assessed non-operative management were included. Results: Sixteen studies met inclusion criteria. Most (14/16) addressed post-cannulation bleeding from AVF/AVG within the dialysis unit. Compared with standard dressings, hemostatic dressings (chitosan-, cellulose- or thrombin-based) decreased post-cannulation bleeding time at arterial and venous site 35.7%-84.0% (P < .05) and 38.5%-78.7% (P < .05), respectively. Use of chitosan-based dressings decreased percentage of patients bleeding 4-min post-cannulation by 16.3%-39.2%. One pilot observational study demonstrated no access thromboses or infections with short-term use of a compression device within the hemodialysis unit. However, the role of compression devices and tourniquets within the dialysis unit remains unclear, despite widespread use. Long-term AVF/AVG survival was not reported in any study. Limited research confirms that devices are effective in prevention of catastrophic out-of-hospital bleeding. It remains uncertain if device availability enhances patient confidence in managing out-of-hospital bleeding. This may impact patient choices around dialysis modality, access and transplant, but this remains uncertain. Conclusions: In hemodialysis patents with bleeding from AVF/AVG, several alternative dressings or devices decrease post-cannulation bleeding time within the hemodialysis unit. Existing research has not established criteria on when it might be appropriate to use specialized dressings. There is very limited research on methods to control bleeding from AVF/AVG outside the hemodialysis unit. More data are required before evidence-based guidelines can be made. Recommendations for future research are provided.

Preliminary study on the computer-based optokinetic nystagmus analyzer to detect the visual acuity of preschool children.

The purpose of this study is to examine the viability, precision, and consistency of a computer-based optokinetic nystagmus analyzer (nystagmus meter) for diagnosing eyesight in preschoolers. A total of 59 subjects who could pass the log of minimum angle of resolution (LogMAR) visual acuity chart were divided into three groups by age, 4-, 5-, and 6-year-old groups, and their visual acuity was tested with nystagmus meter. The percentage of children in each age group that could be detected by nystagmus was recorded along with the differences between these groups. The correlation between the test results from the two methods was found for each age group using the correlation coefficient method. Repeated measurements were used to assess the two visual acuity values of the measured nystagmus, and the repeatability of the two measurement techniques for different age groups was compared. The overall measurability of the visual acuity detected by nystagmus was 93.22%, and the measurability of the 4-, 5-, and 6-year-old groups was 90%, 95%, and 94.74%, respectively. There was no statistically significant difference in the measurability of subjects among all age groups (P = 1.0). The outcomes of the LogMAR visual acuity chart had a negative correlation with the visual acuity measured by the nystagmus meter. The overall correlation coefficient R value was -0.80, and the correlation coefficient R value of the 4-, 5-, and 6-year-old groups was -0.79, -0.76, and -0.87, respectively. The nystagmus meter has good feasibility, accuracy, and stability in visual acuity testing and can be used for visual acuity testing in children.

Vascular calcification in chronic kidney disease associated with pathogenic variants in ABCC6.

Vascular calcification is prevalent in chronic kidney disease (CKD). Genetic causes of CKD account for 10-20% of adult-onset disease. Vascular calcification is thought to be one of the most important risk factors for increased cardiovascular morbidity and mortality in CKD patients and is detectable in 80% of patients with end stage kidney disease (ESKD). Despite the high prevalence of vascular calcification in CKD, no single gene cause has been described. We hypothesized that variants in vascular calcification genes may contribute to disease pathogenesis in CKD, particularly in families who exhibit a predominant vascular calcification phenotype. We developed a list of eight genes that are hypothesized to play a role in vascular calcification due to their involvement in the ectopic calcification pathway: ABCC6, ALPL, ANK1, ENPP1, NT5E, SLC29A1, SLC20A2, and S100A12. With this, we assessed exome data from 77 CKD patients, who remained unsolved following evaluation for all known monogenic causes of CKD. We also analyzed an independent cohort (Ontario Neurodegenerative Disease Research Initiative (ONDRI), n = 520) who were screened for variants in ABCC6 and compared this to a control cohort of healthy adults (n = 52). We identified two CKD families with heterozygous pathogenic variants (R1141X and A667fs) in ABCC6. We identified 10 participants from the ONDRI cohort with heterozygous pathogenic or likely pathogenic variant in ABCC6. Replication in a healthy control cohort did not reveal any variants. Our study provides preliminary data supporting the hypothesis that ABCC6 may play a role in vascular calcification in CKD. By screening CKD patients for genetic causes early in the diagnostic pathway, patients with genetic causes associated with vascular calcification can be preventatively treated with new therapeutics with aims to decrease mortality.

Calcium-channel blocker-clarithromycin drug interactions and acute kidney injury.

IMPORTANCE: Calcium-channel blockers are metabolized by the cytochrome P450 3A4 (CYP3A4; EC 1.14.13.97) enzyme. Blood concentrations of these drugs may rise to harmful levels when CYP3A4 activity is inhibited. Clarithromycin is an inhibitor of CYP3A4 and azithromycin is not, which makes comparisons between these 2 macrolide antibiotics useful in assessing clinically important drug interactions. OBJECTIVE: To characterize the risk of acute adverse events following coprescription of clarithromycin compared with azithromycin in older adults taking a calcium-channel blocker. DESIGN, SETTING, AND PARTICIPANTS: Population-based retrospective cohort study in Ontario, Canada, from 2003 through 2012 of older adults (mean age, 76 years) who were newly coprescribed clarithromycin (n = 96,226) or azithromycin (n = 94,083) while taking a calcium-channel blocker (amlodipine, felodipine, nifedipine, diltiazem, or verapamil). MAIN OUTCOMES AND MEASURES: Hospitalization with acute kidney injury (primary outcome) and hospitalization with hypotension and all-cause mortality (secondary outcomes examined separately). Outcomes were assessed within 30 days of a new coprescription. RESULTS: There were no differences in measured baseline characteristics between the clarithromycin and azithromycin groups. Amlodipine was the most commonly prescribed calcium-channel blocker (more than 50% of patients). Coprescribing clarithromycin vs azithromycin with a calcium-channel blocker was associated with a higher risk of hospitalization with acute kidney injury (420 patients of 96,226 taking clarithromycin [0.44%] vs 208 patients of 94,083 taking azithromycin [0.22%]; absolute risk increase, 0.22% [95% CI, 0.16%-0.27%]; odds ratio [OR], 1.98 [95% CI, 1.68-2.34]). In a subgroup analysis, the risk was highest with dihydropyridines, particularly nifedipine (OR, 5.33 [95% CI, 3.39-8.38]; absolute risk increase, 0.63% [95% CI, 0.49%-0.78%]). Coprescription with clarithromycin was also associated with a higher risk of hospitalization with hypotension (111 patients of 96,226 taking clarithromycin [0.12%] vs 68 patients of 94,083 taking azithromycin [0.07%]; absolute risk increase, 0.04% [95% CI, 0.02%-0.07%]; OR, 1.60 [95% CI, 1.18-2.16]) and all-cause mortality (984 patients of 96,226 taking clarithromycin [1.02%] vs 555 patients of 94,083 taking azithromycin [0.59%]; absolute risk increase, 0.43% [95% CI, 0.35%-0.51%]; OR, 1.74 [95% CI, 1.57-1.93]). CONCLUSIONS AND RELEVANCE: Among older adults taking a calcium-channel blocker, concurrent use of clarithromycin compared with azithromycin was associated with a small but statistically significant greater 30-day risk of hospitalization with acute kidney injury. These findings support current safety warnings regarding concurrent use of CYP3A4 inhibitors and calcium-channel blockers.

Synthesis of cerium oxide embedded perovskite type bismuth ferrite nanocomposites for sonophotocatalysis of aqueous micropollutant ibuprofen.

Ibuprofen is potentially toxic and carcinogenic for freshwater ecosystems and poses a serious threat to human health by affecting kidney function. The present study focused on the sunlight-controlled degradation of ibuprofen from water using a novel magnetically separable cerium oxide-embedded bismuth ferrite heterostructure. Catalysts were synthesized by solvothermal and co-precipitation methods and characterized by X-ray diffractometry, transmission electron microscopy, X-ray photoelectron spectroscopy, UV-vis optical absorption spectroscopy, and nitrogen adsorption. This study investigated the effect of photocatalysis, sonolysis, sonophotolysis, and sonophotocatalysis on the degradation of ibuprofen in water. Pseudo-first-order and second-order kinetics were applied to evaluate the rate of reaction for ibuprofen degradation. The addition of 5% CeO2 to the BiFeO3 significantly increased the surface area and pore volume of bismuth ferrite, which enhanced their photocatalytic degradation efficiency by 2.28 times in terms of ibuprofen mineralization. Sonolysis treatment alone and in combination with photolysis led to the degradation of ibuprofen, but with the formation of intermediate products. Positive synergy was observed when sonolysis was combined with photocatalysis in terms of the mineralization of ibuprofen and the degradation of intermediates along with their parent compound. It was proposed that, compared to photocatalytic mineralization, the ultrasound-assisted advanced oxidation process resulted in the conversion of ibuprofen to its mineralization products.

Machine learning-based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study.

Osteoporosis is a skeletal disease that is commonly seen in older people but often neglected due to its silent nature. To overcome the issue of osteoporosis in men and women, we proposed an advanced prediction model with the help of machine learning techniques which can help to identify the potential occurrence of this bone disease by its advanced screening tools. To achieve more reliable and accurate results, various machine-learning techniques were applied to the presented data sets. Moreover, we also compared the performance of our results with other existing algorithms to solely focus on the advanced features of the proposed methodology. The two data sets, the clinical tests of patients in Taiwan and medical reports of postmenopausal women in Korea through Korean Health and Nutrition Examination Surveys (2010-2011) were considered in this study. To predict bone disorders, we utilized the data about females and developed a system using artificial neural networks, support vector machines, and K-nearest neighbor. To compare the performance of the model Area under the Receiver Operating Characteristic Curve and other evaluation metrics were compared. The achieved results from all the algorithms and compared them with Osteoporosis Self-Assessment Tool for Asians and the results were noticeably better and more reliable than existing systems due to the involvement of ML. Using machine learning techniques to predict these types of diseases is better because physicians and patients can take early action to prevent the consequences in advance.

Impact of COVID-19 lockdown on air quality analyzed through machine learning techniques.

After February 2020, the majority of the world's governments decided to implement a lockdown in order to limit the spread of the deadly COVID-19 virus. This restriction improved air quality by reducing emissions of particular atmospheric pollutants from industrial and vehicular traffic. In this study, we look at how the COVID-19 shutdown influenced the air quality in Lahore, Pakistan. HAC Agri Limited, Dawn Food Head Office, Phase 8-DHA, and Zeenat Block in Lahore were chosen to give historical data on the concentrations of many pollutants, including PM2.5, PM10 (particulate matter), NO2 (nitrogen dioxide), and O3 (ozone). We use a variety of models, including decision tree, SVR, random forest, ARIMA, CNN, N-BEATS, and LSTM, to compare and forecast air quality. Using machine learning methods, we looked at how each pollutant's levels changed during the lockdown. It has been shown that LSTM estimates the amounts of each pollutant during the lockout more precisely than other models. The results show that during the lockdown, the concentration of atmospheric pollutants decreased, and the air quality index improved by around 20%. The results also show a 42% drop in PM2.5 concentration, a 72% drop in PM10 concentration, a 29% drop in NO2 concentration, and an increase of 20% in O3 concentration. The machine learning models are assessed using the RMSE, MAE, and R-SQUARE values. The LSTM measures NO2 at 4.35%, O3 at 8.2%, PM2.5 at 4.46%, and PM10 at 8.58% in terms of MAE. It is observed that the LSTM model outperformed with the fewest errors when the projected values are compared with the actual values.

Prevalence of mobile phone addiction among medical students: a systematic review.

The incidence and factors related to mobile phone addiction among Chinese medical students were analyzed through meta-analysis. Chinese literature databases (such as China Knowledge Network and VIP Information Resource System) and English literature databases (such as PubMed and Web of Science) were searched for cross-sectional studies on the incidence and factors related to mobile phone addiction, and the required data were extracted. Meta-analysis was performed using a random effects model with RevMan 5.3 statistical software, and publication bias was tested with Stata 12.0. A total of 20 studies were included, including 36,365 study subjects. Among them, there were 10,597 cases of mobile phone addiction with an incidence of 29.14%. The results of the meta-analysis showed that the combined OR values (95% CI) of the factors were: gender 1.070 (1.030-1.120), residence 1.118 (1.090-1.146), school type 1.280 (1.241-1.321), mobile phone use time 1.098 (1.068-1.129), sleep quality 1.280 (1.288-1.334), self-perception of learning 0.737 (0.710-0.767), and family relationship 0.821 (0.791-0.852). The study showed that being a male student from cities and towns, being at a vocational college, excessive use of mobile phones, and poor sleep quality were the risk factors for mobile phone addiction among medical students in China. Positive self-perception of learning and family relationships were protective factors, and more related factors are still controversial and need to be further explored and confirmed.

Adverse Event Profile Differences between Trastuzumab Emtansine and Trastuzumab Deruxtecan: A Real-world, Pharmacovigilance Study.

Introduction: Trastuzumab emtansine(T-DM1) and trastuzumab deruxtecan (T-DXd, formerly DS-8201a), the human epidermal growth factor receptor 2 (HER2)-targeted antibody-drug conjugate (ADC), are commonly used in metastatic breast cancer. However, their real-world safety profile has not been adequately compared. Objective: We aimed to investigate the adverse event (AE) profile of T-DM1 and T-DXd reported by the US Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: All indications were searched for T-DM1 and T-DXd, as primary suspected drugs, from FAERS data (January 2004 to June 2023). Disproportionality analyses were performed by reporting odds ratios (ROR) and proportional reporting ratio (PRR). The odds ratio (OR) of fatal AEs associated with T-DM1 and T-DXd under different exposure factors were performed by univariate and multivariate logistical regression analysis. Results: 3723 and 2045 reports of T-DM1 and T-DXd were submitted to FAERS. Finally, 94 and 61 significant signals for T-DM1 and T-DXd were systematically analyzed. The valid AEs with the highest frequency and the strongest signal intensity for T-DM1 were platelet count decreased (n=108) and hepatopulmonary syndrome (ROR=680.42), respectively. Interstitial lung disease (n=262, ROR=82.55) and pneumonitis (n=89, ROR = 48.34) showed both high frequency and strong signal intensity for T-DXd. The proportion of AEs in each SOC system was different. T-DM1 had a greater proportion of valid AEs in the nervous system, musculoskeletal system, hepatobiliary system, ocular system, cardiac system and hematologic system(p<0.05). T-DXd had a greater proportion of valid AEs in the skin disorders, respiratory system, infestations, general system and gastrointestinal system(p<0.05). Furthermore, the analysis of fatal AEs in four systems revealed that T-DXd exhibited a significantly higher proportion of fatal outcomes in the hematologic and respiratory system compared to T-DM1. Conversely, T-DM1 had a significantly higher proportion of fatal outcomes in the hepatobiliary system. Neither T-DM1 nor T-DXd exhibited a high mortality ratio in the cardiac system. Logistic regression analysis indicated that advanced age (≥65 years) and male gender were identified as independent risk factors of fatal AEs for both T-DM1 and T-DXd. Additionally, the drug combination therapy, particularly with a CYP3A4 inhibitor, was found to be a risk factor for fatal AEs specifically related to T-DXd. Conclusions: Hematological and respiratory toxicity of T-DXd and hepatobiliary toxicity of T-DM1 exhibited a high incidence of fatal outcomes. It is crucial to identify high-risk factors and enhance the monitoring of AEs during clinical application.

The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer.

Machine learning (ML) can enhance a dermatologist's work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cancer detection using deep learning to assess the features and advantages of other techniques. Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too.