Psychological status of infertile men during the Coronavirus Disease 2019 Pandemic in China: a cross-sectional investigation.

BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak has had a widespread and profound impact on people's mental health. The factors associated with mental symptoms among men diagnosed with infertility, a disease closely related to psychological conditions, remain unclear. The aim of this study is to investigate the risk factors associated with mental symptoms among infertile Chinese men during the pandemic. RESULTS: A total of 4,098 eligible participants were recruited in this cross-sectional, nationwide study, including 2,034 (49.6%) with primary infertility and 2,064 (50.4%) with secondary infertility. The prevalence of mental health conditions was 36.3%, 39.6%, and 6.7% for anxiety, depression, and post-pandemic stress, respectively. Sexual dysfunction is associated with a higher risk with adjusted odds ratios (ORs) of 1.40 for anxiety, 1.38 for depression, and 2.32 for stress. Men receiving infertility drug therapy displayed a higher risk for anxiety (adjusted OR, 1.31) and depression (adjusted OR, 1.28) symptoms, while those receiving intrauterine insemination had a lower risk of anxiety (adjusted OR, 0.56) and depression (adjusted OR, 0.55) symptoms. CONCLUSION: The COVID-19 pandemic has had a significant psychological impact on infertile men. Several psychologically vulnerable populations were identified, including individuals with sexual dysfunction, respondents receiving infertility drug therapy, and those experiencing control measures for COVID-19. The findings provide a comprehensive profile of the mental health status of infertile Chinese men during the COVID-19 outbreak and provide potential psychological intervention strategies.

RéSUMé: CONTEXTE: L'épidémie de maladie à coronavirus 2019 (COVID-19) a eu un impact étendu et profond sur la santé mentale des gens. Les facteurs associés aux symptômes mentaux chez les hommes diagnostiqués comme infertiles, une maladie étroitement liée aux conditions psychologiques, restent flous. L'objectif de cette étude est d'étudier les facteurs de risque associés aux symptômes mentaux chez les hommes chinois infertiles pendant la pandémie. RéSULTATS: Au total, 4 098 participants admissibles ont été recrutés dans cette étude transversale à l'échelle nationale, dont 2 034 (49,6%) présentaient une infertilité primaire et 2 064 (50,4%) une infertilité secondaire. La prévalence des problèmes de santé mentale était respectivement de 36,3 %, 39,6 % et 6,7 % pour l'anxiété, la dépression, et le stress postpandémique. La dysfonction sexuelle est associée à un risque plus élevé avec des odds ratios ajustés (OR) de 1,40 pour l'anxiété, 1,38 pour la dépression et 2,32 pour le stress. Les hommes recevant un traitement médicamenteux contre l'infertilité présentaient un risque plus élevé de symptômes d'anxiété (OR ajusté, 1,31) et de dépression (OR ajusté, 1,28), alors que ceux dont le traitement consistait à faire des inséminations intra-utérines présentaient un risque plus faible de symptômes d'anxiété (OR ajusté, 0,56) et de dépression (OR ajusté, 0,55). CONCLUSIONS: La pandémie de COVID-19 a eu un impact psychologique important sur les hommes infertiles. Plusieurs populations psychologiquement vulnérables ont été identifiées, notamment les personnes souffrant de dysfonction sexuelle, les hommes recevant un traitement médicamenteux contre l'infertilité, et ceux subissant des mesures de contrôle de la COVID-19. Les résultats fournissent un profil complet de l'état de santé mentale des hommes Chinois infertiles pendant l'épidémie de COVID-19 et fournissent des stratégies potentielles d'intervention psychologique.

Machine learning discovery of distinguishing laboratory features for severity classification of COVID-19 patients

Abstract The exponential spread of COVID-19 worldwide is evident, with devastating outbreaks primarily in the United States, Spain, Italy, the United Kingdom, France, Germany, Turkey and Russia. As of 1 May 2020, a total of 3,308,386 confirmed cases have been reported worldwide, with an accumulative mortality of 233,093. Due to the complexity and uncertainty of the pathology of COVID-19, it is not easy for front-line doctors to categorise severity levels of clinical COVID-19 that are general and severe/critical cases, with consistency. The more than 300 laboratory features, coupled with underlying disease, all combine to complicate proper and rapid patient diagnosis. However, such screening is necessary for early triage, diagnosis, assignment of appropriate level of care facility, and institution of timely intervention. A machine learning analysis was carried out with confirmed COVID-19 patient data from 10 January to 18 February 2020, who were admitted to Tongji Hospital, in Wuhan, China. A softmax neural network-based machine learning model was established to categorise patient severity levels. According to the analysis of 2662 cases using clinical and laboratory data, the present model can be used to reveal the top 30 of more than 300 laboratory features, yielding 86.30% blind test accuracy, 0.8195 F1-score, and 100% consistency using a two-way patient classification of severe/critical to general. For severe/critical cases, F1-score is 0.9081 (i.e. recall is 0.9050, and precision is 0.9113). This model for classification can be accomplished at a mini-second-level computational cost (in contrast to minute-level manual). Based on available COVID-19 patient diagnosis and therapy, an artificial intelligence model paradigm can help doctors quickly classify patients with a high degree of accuracy and 100% consistency to significantly improve diagnostic and classification efficiency. The discovered top 30 laboratory features can be used for greater differentiation to serve as an essential supplement to current guidelines, thus creating a more comprehensive assessment of COVID-19 cases during the early stages of infection. Such early differentiation will help the assignment of the appropriate level of care for individual patients.

Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China.

Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People's Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.

Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be accurately assessed manually. We employed deep learning-based software to aid in detection, localization and quantification of COVID-19 pneumonia. METHODS: A total of 2460 RT-PCR tested SARS-CoV-2-positive patients (1250 men and 1210 women; mean age, 57.7 ± 14.0 years (age range, 11-93 years) were retrospectively identified from Huoshenshan Hospital in Wuhan from February 11 to March 16, 2020. Basic clinical characteristics were reviewed. The uAI Intelligent Assistant Analysis System was used to assess the CT scans. RESULTS: CT scans of 2215 patients (90%) showed multiple lesions of which 36 (1%) and 50 patients (2%) had left and right lung infections, respectively (> 50% of each affected lung's volume), while 27 (1%) had total lung infection (> 50% of the total volume of both lungs). Overall, 298 (12%), 778 (32%) and 1300 (53%) patients exhibited pure ground glass opacities (GGOs), GGOs with sub-solid lesions and GGOs with both sub-solid and solid lesions, respectively. Moreover, 2305 (94%) and 71 (3%) patients presented primarily with GGOs and sub-solid lesions, respectively. Elderly patients (≥ 60 years) were more likely to exhibit sub-solid lesions. The generalized linear mixed model showed that the dorsal segment of the right lower lobe was the favoured site of COVID-19 pneumonia. CONCLUSION: Chest CT combined with analysis by the uAI Intelligent Assistant Analysis System can accurately evaluate pneumonia in COVID-19 patients.

Evaluation of the Adjuvant Efficacy of Natural Herbal Medicine on COVID-19: A Retrospective Matched Case-Control Study

Since the outbreak of Corona Virus Disease 2019 (COVID-19) in Hubei province, the epidemic scale has increased rapidly, and no effective antiviral drug therapy has been identified yet. This study aimed to evaluate the adjuvant efficacy of Natural Herbal Medicine (NHM) combined with Western medicine in the treatment of COVID-19. We performed a retrospective, 1:1 matched, case-control study of the first cohort of hospitalized COVID-19-confirmed cases (January 17, 2020 to January 28, 2020). A total of 22 of the 36 confirmed patients were included in this study, split into two groups of 11: the NHM group (NHM combined standard Western medicine treatment) and control group (standard Western medicine treatment alone). All patients received appropriate supportive care and regular clinical and laboratory monitoring. Main evaluation indicators included improvement of clinical symptoms such as fever, cough and diarrhea after hospitalization;pathogen nucleic acid test result of respiratory tract and fecal specimens of the patient after hospitalization, and change of chest CT examination after hospitalization. The duration of fever in the NHM group (3.4+/-2.4 days) was significantly shorter than that in the control group (5.6+/-2.2 days) (p=0.03). During the whole hospitalization period, the number of cases with diarrhea in the NHM group (two cases) was less than that in the control group (eight cases) (p=0.03). Compared with the control group (7.5+/-1.6), the duration for improvement (DI) of chest CT in the NHM group (5.6+/-2.3) was significantly shorter (p=0.04). Our results suggest that NHM could improve the clinical symptoms of COVID-19 patients and may be effective in treating COVID-19;thus, a larger, prospective, randomized, controlled clinical trial should be conducted to further evaluate the adjuvant efficacy of NHM in the treatment of COVID-19.