1.Impact of long COVID-19 on posttraumatic stress disorderas modified by health literacy: an observational study inVietnam
Han Thi VO ; Tien Duc DAO ; Tuyen Van DUONG ; Tan Thanh NGUYEN ; Binh Nhu DO ; Tinh Xuan DO ; Khue Minh PHAM ; Vinh Hai VU ; Linh Van PHAM ; Lien Thi Hong NGUYEN ; Lan Thi Huong LE ; Hoang Cong NGUYEN ; Nga Hoang DANG ; Trung Huu NGUYEN ; Anh The NGUYEN ; Hoan Van NGUYEN ; Phuoc Ba NGUYEN ; Hoai Thi Thanh NGUYEN ; Thu Thi Minh PHAM ; Thuy Thi LE ; Thao Thi Phuong NGUYEN ; Cuong Quoc TRAN ; Kien Trung NGUYEN
Osong Public Health and Research Perspectives 2024;15(1):33-44
Objectives:
The prevalence of posttraumatic stress disorder (PTSD) has increased, particularly among individuals who have recovered from coronavirus disease 2019 (COVID-19) infection. Health literacy is considered a “social vaccine” that helps people respond effectively to the pandemic. We aimed to investigate the association between long COVID-19 and PTSD, and to examine the modifying role of health literacy in this association.
Methods:
A cross-sectional study was conducted at 18 hospitals and health centers in Vietnamfrom December 2021 to October 2022. We recruited 4,463 individuals who had recovered from COVID-19 infection for at least 4 weeks. Participants provided information about their sociodemographics, clinical parameters, health-related behaviors, health literacy (usingthe 12-item short-form health literacy scale), long COVID-19 symptoms and PTSD (Impact Event Scale-Revised score of 33 or higher). Logistic regression models were used to examine associations and interactions.
Results:
Out of the study sample, 55.9% had long COVID-19 symptoms, and 49.6% had PTSD.Individuals with long COVID-19 symptoms had a higher likelihood of PTSD (odds ratio [OR], 1.86; 95% confidence interval [CI], 1.63–2.12; p < 0.001). Higher health literacy was associated with a lower likelihood of PTSD (OR, 0.98; 95% CI, 0.97–0.99; p = 0.001). Compared to those without long COVID-19 symptoms and the lowest health literacy score, those with long COVID-19 symptoms and a 1-point health literacy increment had a 3% lower likelihood of PTSD (OR, 0.97; 95% CI, 0.96–0.99; p = 0.001).
Conclusion
Health literacy was found to be a protective factor against PTSD and modified the negative impact of long COVID-19 symptoms on PTSD.
2.Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics
Ramy Abou GHAYDA ; Rossella CANNARELLA ; Aldo E. CALOGERO ; Rupin SHAH ; Amarnath RAMBHATLA ; Wael ZOHDY ; Parviz KAVOUSSI ; Tomer AVIDOR-REISS ; Florence BOITRELLE ; Taymour MOSTAFA ; Ramadan SALEH ; Tuncay TOPRAK ; Ponco BIROWO ; Gianmaria SALVIO ; Gokhan CALIK ; Shinnosuke KURODA ; Raneen Sawaid KAIYAL ; Imad ZIOUZIOU ; Andrea CRAFA ; Nguyen Ho Vinh PHUOC ; Giorgio I. RUSSO ; Damayanthi DURAIRAJANAYAGAM ; Manaf AL-HASHIMI ; Taha Abo-Almagd Abdel-Meguid HAMODA ; Germar-Michael PINGGERA ; Ricky ADRIANSJAH ; Israel Maldonado ROSAS ; Mohamed ARAFA ; Eric CHUNG ; Widi ATMOKO ; Lucia ROCCO ; Haocheng LIN ; Eric HUYGHE ; Priyank KOTHARI ; Jesus Fernando Solorzano VAZQUEZ ; Fotios DIMITRIADIS ; Nicolas GARRIDO ; Sheryl HOMA ; Marco FALCONE ; Marjan SABBAGHIAN ; Hussein KANDIL ; Edmund KO ; Marlon MARTINEZ ; Quang NGUYEN ; Ahmed M. HARRAZ ; Ege Can SEREFOGLU ; Vilvapathy Senguttuvan KARTHIKEYAN ; Dung Mai Ba TIEN ; Sunil JINDAL ; Sava MICIC ; Marina BELLAVIA ; Hamed ALALI ; Nazim GHERABI ; Sheena LEWIS ; Hyun Jun PARK ; Mara SIMOPOULOU ; Hassan SALLAM ; Liliana RAMIREZ ; Giovanni COLPI ; Ashok AGARWAL ;
The World Journal of Men's Health 2024;42(1):39-61
Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.