Hemograms and serial hemogram-derived ratios in survivors and non-survivors of COVID-19 in Campinas, Brazil

Introduction The hemogram and hemogram-derivative ratios (HDRs) are becoming markers of the severity and mortality of COVID-19. We evaluated the hemograms and serial weekly HDRs [neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet-lymphocyte ratio (PLR), neutrophil-platelet ratio (NPR) and systemic immune-inflammatory index (SII)] in the survivors and non-survivors of COVID-19. Methods We retrospectively reviewed the medical notes and serial hemograms of real-time reverse-transcription polymerase chain reaction (RT-PCR)-confirmed COVID-19 adults hospitalized from April 2020 to March 2021 from the time of diagnosis to the 3rd week of diagnosis. Results Of the 320 adults, 257 (80.3%) were survivors and had a lower mean age than the non-survivors (57.73 vs. 64.65 years, p < 0.001). At diagnosis, the non-survivors had lower hematocrit (p = 0.021), and lymphocyte (p = 0.002) and basophil (p = 0.049) counts and the hematocrit showed a p-value (Is this what you meant???) of 0.021); higher NLR (p < 0.001), PLR (p = 0.047), NPR (p = 0.022) and SII (p = 0.022). Using general linear models, the survivors and non-survivors showed significant variations with weekly lymphocyte count (p < 0.001), neutrophil count (p = 0.005), NLR (p = 0.009), MLR (p = 0.010) and PLR (p = 0.035). All HDRs remained higher in the non-survivors in the 2nd week and 3rd week of diagnosis and the HDRs were higher in the intubated patients than in the non-intubated patients. The NLR and SII were more efficient predictors of mortality in COVID-19 patients. Conclusions This study shows that serial lymphocyte and neutrophil counts, NLR, PLR, MLR, NPR and SII could serve as good and easily accessible markers of severity and predictors of outcomes in COVID-19 patients and should be used for the monitoring of treatment response.

Honduras HIV cohort: HLA class I and CCR5-Δ32 profiles and their associations with HIV disease outcome.

IMPORTANCE: We identify both canonical and novel human leukocyte antigen (HLA)-HIV associations, providing a first step toward improved understanding of HIV immune control among the understudied Honduras Mestizo population. Our results are relevant to understanding the protective or detrimental effects of HLA subtypes in Latin America because their unique HLA diversity poses challenges for designing vaccines against HIV and interpreting results from such vaccine trials. Likewise, the description of the HLA profile in an understudied population that shows a unique HLA immunogenetic background is not only relevant for HIV immunology but also relevant in population genetics, molecular anthropology, susceptibility to other infections, autoimmune diseases, and allograft transplantation.

Epidemiological and clinical features of the 2016-2018 Zika virus outbreak in northern Argentina.

BACKGROUND: During the American epidemic, Zika virus (ZIKV) expanded rapidly through dengue virus (DENV)-endemic regions. We analyzed the presentation of ZIKV infection in patients from the City of Orán, Argentina, and compared some of its features with dengue presentation in the same region. METHODS: A retrospective study was conducted at San Vicente de Paul Hospital during 2016-2018. Clinical and demographic characteristics, pre-existing immunity to DENV, viral load and type I interferon (IFN) responses were studied in 63 patients with ZIKV infection. RESULTS: Clinical manifestations of ZIKV infection were generally mild compared with dengue, although rash (p<0.001) and itching (p<0.001) were significantly more prevalent in ZIKV patients. ZIKV patients aged <15 y manifested relatively mild disease compared with older ZIKV patients, showing a decreased prevalence of headache (p=0.008), retro-orbital pain (p=0.001) and arthralgia (p=0.001). Increased Zika incidence was observed in female patients (60.3%). Serum viral load was low to undetectable in ZIKV patients and was not associated with serum anti-DENV IgG titers. Interferon-α and IFN-ß serum levels did not correlate with serum viral load in ZIKV patients. CONCLUSIONS: Clinical presentation of ZIKV and DENV infections is largely overlapping, presenting a challenge for diagnosis and risk assessment for uniquely at-risk populations.

Hemograms and serial hemogram-derived ratios in survivors and non-survivors of COVID-19 in Campinas, Brazil.

Introduction: The hemogram and hemogram-derivative ratios (HDRs) are becoming markers of the severity and mortality of COVID-19. We evaluated the hemograms and serial weekly HDRs [neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet-lymphocyte ratio (PLR), neutrophil-platelet ratio (NPR) and systemic immune-inflammatory index (SII)] in the survivors and non-survivors of COVID-19. Methods: We retrospectively reviewed the medical notes and serial hemograms of real-time reverse-transcription polymerase chain reaction (RT-PCR)-confirmed COVID-19 adults hospitalized from April 2020 to March 2021 from the time of diagnosis to the 3rd week of diagnosis. Results: Of the 320 adults, 257 (80.3%) were survivors and had a lower mean age than the non-survivors (57.73 vs. 64.65 years, p < 0.001). At diagnosis, the non-survivors had lower lymphocyte (p = 0.002) and basophil (p = 0.049) counts and the hematocrit showed a p-value (Is this what you meant???) of 0.021); higher NLR (p < 0.001), PLR (p = 0.047), NPR (p = 0.022) and SII (p = 0.022). Using general linear models, the survivors and non-survivors showed significant variations with weekly lymphocyte count (p < 0.001), neutrophil count (p = 0.005), NLR (p = 0.009), MLR (p = 0.010) and PLR (p = 0.035). All HDRs remained higher in the non-survivors in the 2nd week and 3rd week of diagnosis and the HDRs were higher in the intubated patients than in the non-intubated patients. The NLR and SII were more efficient predictors of mortality in COVID-19 patients. Conclusions: This study shows that serial lymphocyte and neutrophil counts, NLR, PLR, MLR, NPR and SII could serve as good and easily accessible markers of severity and predictors of outcomes in COVID-19 patients and should be used for the monitoring of treatment response.

Function Over Mass: A Meta-Analysis on the Importance of Skeletal Muscle Quality in COVID-19 Patients.

COVID-19 caused by SARS-CoV-2 infection is a highly contagious disease affecting both the higher and lower portions of the respiratory tract. This disease reached over 265 million people and has been responsible for over 5.25 million deaths worldwide. Skeletal muscle quality and total mass seem to be predictive of COVID-19 outcome. This systematic review aimed at providing a critical analysis of the studies published so far reporting on skeletal muscle mass in patients with COVID-19, with the intent of examining the eventual association between muscle status and disease severity. A meta-analysis was performed to evaluate whether skeletal muscle quantity, quality and function were related to disease severity. Systematic reviews and meta-analyses were conducted according to the guidelines of the Cochrane Handbook for Systematic Reviews of Interventions and reported according to the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guide. From a total of 1,056 references found, 480 were selected after removing duplicates. Finally, only 7 met the specified inclusion criteria. The results of this meta-analysis showed that skeletal muscle quality, rather than quantity, was associated with COVID-19 severity, as confirmed by lower skeletal muscle density and lower handgrip strength in patients with severe disease. Muscle function assessment can thus be a valuable tool with prognostic value in COVID-19.

Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data.

The first officially registered case of COVID-19 in Brazil was on February 26, 2020. Since then, the situation has worsened with more than 672, 000 confirmed cases and at least 36, 000 reported deaths by June 2020. Accurate diagnosis of patients with COVID-19 is extremely important to offer adequate treatment, and avoid overloading the healthcare system. Characteristics of patients such as age, comorbidities and varied clinical symptoms can help in classifying the level of infection severity, predict the disease outcome and the need for hospitalization. Here, we present a study to predict a poor prognosis in positive COVID-19 patients and possible outcomes using machine learning. The study dataset comprises information of 8, 443 patients concerning closed cases due to cure or death. Our experimental results show the disease outcome can be predicted with a Receiver Operating Characteristic AUC of 0.92, Sensitivity of 0.88 and Specificity of 0.82 for the best prediction model. This is a preliminary retrospective study which can be improved with the inclusion of further data. Conclusion: Machine learning techniques fed with demographic and clinical data along with comorbidities of the patients can assist in the prognostic prediction and physician decision-making, allowing a faster response and contributing to the non-overload of healthcare systems.

Tissue Proteases and Immune Responses: Influencing Factors of COVID-19 Severity and Mortality.

The coronavirus disease 19 (COVID-19) is caused by the highly transmissible severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has affected the global population despite socioeconomic status and amazed surveillance agencies for its incidence, mortality, and recovery rates. COVID-19 affects all age groups; however, it is suggested to progress into severe disease and cause mortality in over 10% of the confirmed cases, depending on the individual characteristics of the affected population. One of the biggest unanswered questions it is why only some individuals develop into the severe stages of the disease. Current data indicate that most of the critically ill are the elderly or those with comorbidities such as hypertension, diabetes, and asthma. However, it has been noted that, in some populations, severe disease is mostly observed in much younger individuals (<60-years old) with no reported underlying medical conditions. Certainly, many factors may contribute to disease severity including intrinsic host factors such as genetic variants, the expression levels of tissue proteins, among others. Considering all these aspects, this review aims to discuss how the expression levels of tissue proteases and the different profiles of immune responses influence the susceptibility to COVID-19 as well as disease severity and outcome.

Retrospective analysis of breast cancer prognosis among young and older women in a Brazilian cohort of 738 patients, 1985-2002.

Invasive breast cancer (BC) is infrequent among women aged ≤40 years, however, the disease outlook in these younger patients is generally worse than among older women. The present study aimed to compare socio-demographic, clinical and pathological characteristics, and their association with long-term survival, between two random cohorts of young (≤40 years) and older (50-69 years) Brazilian patients with BC. The cohort comprised of 738 randomly selected women who were diagnosed with BC at Barretos Cancer Hospital, Pio XII Foundation (Barretos, Brazil) between January 1985 and December 2002; the patients included young women (n=376) and older women (n=362). The current analysis suggested that BC in young women is associated with numerous pathological features of aggressiveness. Second cancer and bilateral BC were independent predictors of a poor outcome in the younger group. Furthermore, C-erB-2 was positively correlated with poor outcome in the older group, whereas estrogen receptor status and TNM stage were associated with disease prognosis in both groups. The overall survival rates of the two age groups were similar except when analyzed according the treatment period (1997-2002). Although patients aged ≤40 years harbored tumors with more aggressive clinicopathological characteristics, these characteristics were not independent predictors of overall survival. The present study indicates that medical advances associated with prevention of breast cancer may improve screening programs, which may therefore increase early diagnosis and subsequently lower mortality rates.