Depression, anxiety, and stress in breast cancer patients: prevalence, associated risk factors, and clinical correlates by Amirhossein Hessami, Erfan Ghadirzadeh, Shayan Ashrafi, Farima Taghavi, Forouzan Elyasi, Mobina Gheibi, Ehsan Zaboli, Reza Alizadeh-Navaei, Fatemeh Akbari Gelvardi, Akbar Hedayatizadeh-Omran, Mahmood Moosazadeh

“…Data were collected via phone interviews and web-based surveys, and analyzed using SPSS software version 27. Demographic, clinical, and socioeconomic determinants were evaluated via logistic regression. …”
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Epidemiology and risk factors for mortality in clostridial bacteremia in Japan: A retrospective multicenter observational study by Aiko Okazaki, Shu Okugawa, Tatsuya Kobayashi, Miki Kawada, Kyotaro Kawase, Shin Nakayama, Yoshitaka Wakabayashi, Takatoshi Kitazawa, Riko Takezawa, Keita Tatsuno, Saho Koyano, Yoshimi Higurashi, Mahoko Ikeda, Sohei Harada, Takeya Tsutsumi

“…Multivariate logistic regression analysis identified independent risk factors for in-hospital mortality. …”
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Association of Clinical, Psychosocial, and Social Determinants of Health Factors and Liver Transplantation and Waitlist Removal for MASH by Kelly Torosian, MD, Fanny Delebecque, BS, Deyna Arellano, BS, Irine Vodkin, MD, Veeral Ajmera, MD, MAS, Monica Tincopa, MD, MSc

“…Demographic, clinical, psychosocial and SDOH characteristics were evaluated. Factors associated with LT and LT waitlist removal were analyzed using univariate and multivariate logistic regression. …”
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Risk Factors Associated with Impairment in Pulmonary Diffusing Capacity among Patients with Noncystic Fibrosis Bronchiectasis by Kaijun Zhang, Xin Zou, Zhiyi Ma, Xiaohong Liu, Chencheng Qiu, Lingyan Xie, Zhaosheng Lin, Saiyu Li, Yongming Wu

“…This study aims to investigate the risk factors associated with impaired pulmonary diffusing capacity among patients with noncystic fibrosis bronchiectasis (NCFB) and compare the predictive value of several scoring systems for the impairment in these patients. …”
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Risk factors for growth retardation in children with juvenile idiopathic arthritis: a case-control study by Ying Zhang, Liping Song, Weihu Ma, Ye Wang, Peipei Wang, Xiaolin Zhang, Zhe Zhang, Yuan Zhang, Qianhui Yu, Yang Yu, Lin Zhang

“…Logistic regression and receiver operating characteristic (ROC) curve analyses were employed to identify and assess the factors associated with growth retardation. …”
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Fetal outcomes and associated factors of antepartum hemorrhage in Ethiopia: A systematic review and meta-analysis. by Gemeda Wakgari Kitil, Adamu Ambachew Shibabaw, Eyob Getachew, Amlaku Nigusie Yirsaw, Berihun Agegn Mengistie, Gebeyehu Lakew, Gebrehiwot Berie Mekonnen, Solomon Seyife Alemu, Firomsa Bekele, Lema Fikadu Wedajo, Addisalem Workie Demsash, Wubishet Gezimu, Mohammedamin Hajure Jarso, Geleta Nenko Dube, Fikadu Wake Butta, Alex Ayenew Chereka

“…Despite the gravity of this condition, there is a lack of synthesized evidence on its prevalence and the associated risk factors specific to the Ethiopian context. This systematic review and meta-analysis aim to consolidate existing research on the fetal outcomes of APH and identify the key factors contributing to its incidence and severity in Ethiopia.…”
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Prevalence of malaria, anemia and associated factors among school children in Hawassa city, Sidama, Ethiopia. by Alia Gena, Solomon Asnake, Tadesse Menjetta

“…A digital hemoglobinometer was used to determine hemoglobin levels and assess anemia prevalence. Data were analyzed using SPSS version 27, with bivariate and multivariate logistic regression performed. …”
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Insights Into Non-Alcoholic Fatty Liver Disease and Diabetes Mellitus in Somalia: Prevalence and Risk Factors by Jeele MOO, Mohamed HN, Addow ROB, Hassan MO, Adam BA

“…NAFLD displayed a robust correlation with insulin resistance (OR: 52.04), with hypertension (OR: 20.091) and hyperlipidemia (OR: 2.528) following shortly thereafter. These factors collectively account for 57% of the variance in NAFLD, as indicated by multiple regression analysis (R2 = 0.57, F (6,823) = 184.302, p < 0.001).Conclusion: This investigation emphasizes the presence of a high prevalence of NAFLD among T2DM patients in Mogadishu, Somalia, 53.8% which is substantially influenced by hypertension, insulin resistance, and hyperlipidemia. …”
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A Study on the Factors Influencing Sunlight in Block Layout: A Case Study of Barcelona Sample by Yunan Zhang, Wenxuan Chen, Kaidi Zhu

“…Based on the simulation results, this paper proposes a regression model for the influencing factors of the neighborhood-style layout. …”
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Food security and its determinants among rural households in Gambella region, Ethiopia by Amanuel Kussia Guyalo

“…The aim of the research is to analyze demographic, socioeconomic, environmental, and institutional factors affecting the food security status of rural households in Gambella region. …”
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Analysis of influencing factors and construct a prediction model for postoperative intestinal obstruction of colorectal cancer by Wang Zhen, Chen Linyun, Wang Lin, He Hongqin, Mou Kaidong, Liu Feng

“…[Objectives] To analyze the influencing factors for postoperative intestinal obstruction of colorectal cancer and construct a prediction model. …”
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A novel AI-driven model for student dropout risk analysis with explainable AI insights by Sumaya Mustofa, Yousuf Rayhan Emon, Sajib Bin Mamun, Shabnur Anonna Akhy, Md Taimur Ahad

“…., depression or persistent failure), and health issues is a growing concern for governments, educators, and guardians. Identifying and analyzing the factors contributing to student dropout is crucial. …”
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Analysis of the State of Highways of Regional Importance in the Russian Federation by S. V. Polyanskaya, D. D. Ulzetueva

“…The purpose of the article is to analyze the regional road network of the Russian Federation, to identify the main factors affecting its condition depending on the federal district. …”
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COMPARISON OF SURVIVAL ANALYSIS USING ACCELERATED FAILURE TIME MODEL AND COX MODEL FOR RECIDIVIST CASE by Nuraziza Arfan, Asrul Irfanullah, Muhammad Rozzaq Hamidi, Utriweni Mukhaiyar

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Factors Influencing Smart Phone Buying Behavior of Consumers: Implications for Building a Marketing Strategy by Brahim Boutaleb

“…Regression model analysis revealed that personal factors, external factors and gender accounted for 41.7% of the variance in buying decisions and were statistically significant. …”
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Cerebral Microhemorrhages in Children With Congenital Heart Disease: Prevalence, Risk Factors, and Association With Neurodevelopmental Outcomes by Kristen N. Andersen, Sicong Yao, Brian R. White, Marin Jacobwitz, Jake Breimann, Jharna Jahnavi, Alexander Schmidt, Wesley B. Baker, Tiffany S. Ko, J. William Gaynor, Arastoo Vossough, Rui Xiao, Daniel J. Licht, Evelyn K. Shih

“…Methods and Results One hundred ninety‐two infants with congenital heart disease undergoing cardiac surgery with cardiopulmonary bypass were evaluated with pre‐ (n=183) and/or postoperative (n=162) magnetic resonance imaging. Perioperative risk factors for CMH and neurodevelopmental outcomes were analyzed using linear regression. …”
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Factors associated with antiretroviral therapy default in the 34-Military Hospital, Sierra Leone, 2017-2020 by Henry Bangura, Fatmata Bangura, Jean Leonard Hakizimana, Adel Hussein Elduma, Gebrekrstos Negash Gebru

“…Defaulting on ART means an HIV-positive patient who has been receiving treatment but misses two or more monthly clinical appointments. We analyzed ART data of people living with HIV to assess factors associated with clients defaulting to ART in the 34-military hospital, in Sierra Leone. …”
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Risk factors and prediction model construction of urinary tract infection in elderly patients with diabetic nephropathy by Cong Feng, Bo Yang, Linlang Liang

“…[Methods] From June 2018 to June 2023, a total of 240 elderly patients with type 2 diabetic nephropathy who visited General Hospital of Northern Theater Command of Chinese PLArmy Hospital were research object, of which 168 were included in disease group 1 and 72 were included in disease group 2.Disease group 1 included 52 cases of urinary tract infections (combined group) and 116 cases of non infection (non combined group); The disease group 2 included 26 cases of infection and 46 cases of non infection.Baseline and laboratory data were compared between disease group 1 and disease group 2, and between the combined group and non combined group; Multivariate logistic regression was applied to analyze the risk factors of urinary tract infections; The nomogram model was constructed based on the selected risk factors and validated, and consistency was checked by generating calibration curves. …”
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Bridging animal models and humans: neuroimaging as intermediate phenotypes linking genetic or stress factors to anhedonia by Huiling Guo, Yao Xiao, Shuai Dong, Jingyu Yang, Pengfei Zhao, Tongtong Zhao, Aoling Cai, Lili Tang, Juan Liu, Hui Wang, Ruifang Hua, Rongxun Liu, Yange Wei, Dandan Sun, Zhongchun Liu, Mingrui Xia, Yong He, Yankun Wu, Tianmei Si, Fay Y. Womer, Fuqiang Xu, Yanqing Tang, Jie Wang, Weixiong Zhang, Xizhe Zhang, Fei Wang

“…To assess the genetic predispositions and stress-related changes associated with these subtypes, we analyzed genotype and metabolite data. Linear regression was employed to determine which neuroimaging features predicted core depression symptoms across species. …”
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Changes of serum tumor marker levels and analysis of risk factors in patients with chronic kidney disease by YU Jun-nan, LIU Cai-xia, ZHAO Nan, SUN Dong

“…Objective To investigate whether the expression of serum tumor markers in patients with chronic kidney disease(CKD)has changed,and to analyze the risk factors influencing the changes.Methods The serum tumor markers[including α-fetoprotein(AFP),carcinoembryonic antigen(CEA),CA125,CA199,CA153,TPSA],albumin(Alb),serum creatinine,hemoglobin levels,age and glomerular filtration rate(GFR)of 427 CKD patients and 420 healthy medical examiners from the Affiliated Hospital of Xuzhou Medical University were collected and compared for differences between the two groups.The relevant factors that lead to changes in tumor markers in patients with CKD were screened out by univariate analysis,and multi-factor logistic regression analysis were used to identify risk factors for elevated tumor indicators.Results There was no significant difference in serum TPSA between the CKD group and the healthy control group(P<0.05).The AFP level was lower than that of the healthy control group,and the difference was statistically significant(P<0.01).The levels of CEA,CA125,CA199,and CA153 were higher than those in the healthy control group,with statistically significantdifferences(P<0.01).Logistic regression analysis showed that decreased glomerular filtration rate was a risk factor for CEA elevation,serum albumin and hemoglobin decline were risk factors for CA125 elevation,serum albumin decrease was a risk factor for CA199 and CA153 elevation,and age was a risk factor for elevated TPSA.Conclusions The application value of TPSA in CKD patients is the same as that in the normal population.In the diagnosis of related tumors based on serum tumor indicators,a comprehensive analysis must be done in conjunction with factors such as patients’ age,renal function,serum albumin,and hemoglobin levels.…”
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