SUMMARY: Severe COVID-19 patients account for most of the mortality of this disease. Early detection and effective treatment of severe patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model correctly classified severe patients with an accuracy of 93.5%, and was further validated using ten independent patients, seven of which were correctly classified. We identified molecular changes in the sera of COVID-19 patients implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study shows that it is possible to predict progression to severe COVID-19 disease using serum protein and metabolite biomarkers. Our data also uncovered molecular pathophysiology of COVID-19 with potential for developing anti-viral therapies.

Hyperuricaemia is common among obese children and adolescents, and is closely related to insulin resistance. The aim of this study was to explore the relationships between youth insulin resistance and hyperuricaemia, as well as their relationships with lifestyle factors in youths, to provide early guidance on the risk factors for hyperuricaemia in adolescents. This study included 233 adolescents aged 10 to 20 years. Insulin resistance was evaluated via the homeostasis model assessment-insulin resistance (HOMA-IR) method. Binary logistic regression analysis was used to assess the associations of HOMA-IR with hyperuricaemia status and serum uric acid (UA) levels. The participants were subsequently divided into two groups, the noninsulin resistant group (HOMA-IR ≤ 3.2) and the insulin resistant group (HOMA-IR > 3.2), to further explore the factors that may affect the serum UA level. Finally, the predictive ability of different indicators of hyperuricaemia was evaluated via the ROC curve. Binary logistic regression analysis revealed a significant increase in the risk of developing hyperuricaemia for individuals with elevated HOMA-IR (p < 0.001) and insulin resistance (p < 0.01). Spearman’s correlation analysis revealed a significant positive linear correlation between HOMA-IR and serum UA levels (r = 0.4652, p < 0.001). Among insulin-resistant adolescents, UA levels were positively correlated with weight ratings, frequency of staying up late, and sugary beverages intake. Notably, individuals who engaged in 1–3 h of weekly exercise had the lowest UA levels. The area under the ROC curve for HOMA-IR was 0.847 (cut-off value = 2.165, p < 0.001), and the optimal prediction model included HOMA-IR, BMI z-score, and other lifestyle factors (AUC: 0.870, p < 0.001)). HOMA-IR was identified as an independent risk factor for the development of hyperuricaemia and could be used as a sensitive indicator for the prediction its development in adolescents. In insulin-resistant adolescents with hyperuricaemia, maintaining normal weight, engaging in physical exercise for 1–3 h per week, avoiding staying up late and limiting sugary beverages intake are recommended to reduce the prevalence of hyperuricaemia among adolescents.

According to previous studies, mental status in 1-year COVID-19 survivors might range from 6–43%. Longer-term psychological consequences in recovered COVID-19 subjects are unknown, so we analyzed longer-term quality of life and mental status in recovered COVID-19 subjects at 2 years after infection. Among 144 recovered COVID-19 subjects in the Taizhou region, 73 and 45 completed face-to-face follow-ups at the first year and second year after infection, respectively, with a 61.7% follow-up rate. The questionnaire, which was administered at both follow-ups, included questions about quality of life, psychological health, and post-traumatic stress disorder (PTSD). The Mann-Whitney U test was used to the differences of each scale between the first and second year. Among the 45 people who completed both follow-up visits, the incidence of psychological problems was 4.4% (2/45) in the first year, and no new psychological abnormalities were observed in the second year. Quality of life improved, while the General Health Questionnaire (GHQ-12) and Impact of Event Scale-Revised (IES-R) scores did not improve over time. The incidence of mental disorders was lower than those in previous studies. Multidisciplinary management for COVID-19 in this study hospital may have reduced the frequency to a certain extent. However, among those with mental health problems, such problems may exist for a long time, and long-term attention should be given to the psychological status of recovered COVID-19 subjects.

Purpose: Overall survival (OS) in postoperative breast cancer patients is influenced by various clinicopathological features. Current prognostic methods, such as the 7th edition of AJCC staging, have limitations. This study aims to construct and validate a comprehensive nomogram integrating multiple clinicopathological features to predict OS more accurately in breast cancer patients.Methods: We identified 60,445 .female patients who underwent breast cancer surgery between January 1, 2011, and December 31, 2015, from the Surveillance, Epidemiology, and End Results (SEER) database, randomly divided into training and internal validation cohorts. Additionally, data from 332 breast cancer surgery patients from four hospitals in Taizhou, Zhejiang Province, were included as an external validation cohort. Kaplan-Meier analysis assessed the impact of clinicopathological features on OS, and multivariable Cox regression identified independent prognostic factors. A nomogram based on these factors was constructed to predict 1-, 3-, and 5-year OS. Model predictive performance was evaluated using C-index, AUC, calibration curves, and decision curves during internal and external validation.Results: Multivariable Cox regression analysis identified age, pathological grade, AJCC stage, ER status, PR status, and HER2 status as independent prognostic factors used in the nomogram construction. The nomogram achieved a C-index of 0.724 (95% CI, 0.716-0.732) in the training cohorts, 0.717 (95% CI, 0.705-0.729) in the internal validation cohorts, and 0.793 (95% CI, 0.724-0.862) in the external validation cohorts, indicating strong discriminative ability. Calibration curves demonstrated good agreement between predicted and observed outcomes in all validation cohorts. Decision curve analysis showed that the nomogram provided maximum net benefit across all validation cohorts.Conclusion: The nomogram developed in this study integrates multiple clinicopathological features and provides a convenient and accurate tool for predicting individualized OS in breast cancer patients. This tool can optimize treatment strategies and improve patient prognosis.

Serum antibodies IgM and IgG are elevated during Coronavirus Disease 2019 (COVID-19) to defend against viral attacks. Atypical results such as negative and abnormally high antibody expression were frequently observed whereas the underlying molecular mechanisms are elusive. In our cohort of 144 COVID-19 patients, 3.5% were both IgM and IgG negative, whereas 29.2% remained only IgM negative. The remaining patients exhibited positive IgM and IgG expression, with 9.3% of them exhibiting over 20-fold higher titers of IgM than the others at their plateau. IgG titers in all of them were significantly boosted after vaccination in the second year. To investigate the underlying molecular mechanisms, we classed the patients into four groups with diverse serological patterns and analyzed their 2-year clinical indicators. Additionally, we collected 111 serum samples for TMTpro-based longitudinal proteomic profiling and characterized 1494 proteins in total. We found that the continuously negative IgM and IgG expression during COVID-19 were associated with mild inflammatory reactions and high T cell responses. Low levels of serum IgD, inferior complement 1 activation of complement cascades, and insufficient cellular immune responses might collectively lead to compensatory serological responses, causing overexpression of IgM. Serum CD163 was positively correlated with antibody titers during seroconversion. This study suggests that patients with negative serology still developed cellular immunity for viral defense and that high titers of IgM might not be favorable to COVID-19 recovery.

Objective: Facing the challenge to manage the SARS-CoV-2 RNA re-positive in discharged COVID-19 patients, it is necessary to explore the limited early risk factors for identifying SARS-CoV-2 RNA re-positive. The triglyceride and glucose index (TyG) has been developed as a surrogate marker of insulin resistance. This study aims to evaluate the correlation of the TyG index with the re-positive of COVID-19. Methods: A total of 144 COVID-19 patients from Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University (China) were enrolled in this study. All of them were discharged after recovery according to the guidelines. We compared the clinical characteristics and laboratory indexes of re-positive and non-re-positive COVID-19 patients, and analyzed the early risk factors for identifying SARS-CoV-2 RNA re-positive. Results: During the follow-up, a total of 18 patients were tested re-positive for SARS-CoV-2 RNA. Re-positive COVID-19 patients had higher proportion of abidol (P=0.018), antibiotic use (P=0.024) and hepatitis-based diseases (P=0.042), and higher heart rate (P=0.011) at admission (P=0.026), while lower TyG index (P=0.036), eGFR (P=0.034), TG (P=0.015) and C1q (P=0.023). Multivariate logistic regression analysis showed that TyG index was an independent risk factor for the re-positive of SARS-CoV-2 RNA (P=0.005). TyG index was significantly correlated with Glu (P< 0.001), TG (P< 0.001) and HDL-C (P< 0.001). In addition, it was found that TyG index decreased at SARS-CoV-2 RNA positive stage and increased at negative stage (P< 0.05). Conclusion: TyG index may be a valuable marker for identifying the re-positive of COVID-19 patients and may play a role in determining the stage of the patient’s disease. We hope to provide a reliable theoretical basis for clinical prediction and effective control of re-positive episodes, and to provide a breakthrough for further research on the causes of re-positive episodes and the immune mechanism of the virus. Graphical Abstract: