Correlation Analysis on Total Lymphocyte Count and CD4 Count in HIV-infected Patients: A Retrospective Evaluation

CD4 count is the standard method for determining eligibility for highly active antiretroviral therapy （HAART） and monitoring HIV/AIDS disease progression, but it is not widely available in resource-limited settings. This study examined the correlation between total lymphocyte count （TLC） and CD4 count of HIV-infected patients before and after HAART, and assessed the thresholds of TLC for making decisions about the initiation and for monitoring HAART. A retrospective study was performed, and 665 HIV-infected patients with TLC and CD4 count from four counties （Shangcai, Queshan, Shenqiu and Weishi） were included in the study. Pearson correlation and receiver operating characteristic （ROC） were used. TLC and CD4 count after HAART was significantly increased as compared with pre-HAART （P〈0.01）. An overall positive correlation was noted between TLC and CD4 count （pre-HAART, r=0.73, P=0.0001; follow-up HAART, r=0.56, P=0.0001）. The ROC curve between TLC and CD4 count showed that TLC ≤ 1200 cells/mm3 could predict CD4 〈 200 cells/mm3 with a sensitivity of 71.12%, specificity of 66.35% at pre-HAART. After 12-month HAART, the optimum prediction for CD4 count 〈 200 cells/mm3 was a TLC ≤ 1300 cells/mm3, with a sensitivity of 63.27%, and a specificity of 74.84%. Further finding indicated that TLC change was positively correlated to CD4 change （r=0.77, P=0.0001） at the time point of 12-month treatment, and the best prediction point of TLC change for CD4 increasing was 135 cells/mm3. TLC and its change can be used as a surrogate marker for CD4 count and its change of HIV-infected individuals for making decisions about the initiation and for monitoring HAART in resource-limited settings.

Lymphocyte count predicts the severity of COVID-19:Evidence from a meta-analysis

BACKGROUND In December 2019,coronavirus disease 2019(COVID-19)was reported firstly in Wuhan,China.COVID-19 is currently a global pandemic.AIM To assess the suitability of lymphocyte count as a biomarker of COVID-19 severity.METHODS Five literature databases(PubMed/MEDLINE,Web of Science,Google Scholar,Embase,and Scopus)were searched to identify eligible articles.A meta-analysis was performed to calculate the standard mean difference(SMD)and 95%confidence interval(CI)of lymphocyte counts in coronaviral pneumonia cases.RESULTS Eight studies,including 1057 patients,were integrated in the meta-analysis.Lymphocyte counts were associated with severe coronavirus(CoV)infection(SMD=1.35,95%CI:1.97 to 0.37,P<0.001,I2=92.6%).In the subgroup analysis stratified by prognosis,lymphocytes were associated with CoV infection mortality(n=2,SMD=0.42,95%CI:0.66 to 0.19,P<0.001,I2=0.0%),severity(n=2,SMD=0.93,95%CI:1.20 to 0.67,P<0.001,I2=0.0%),and diagnostic rate(n=4,SMD=2.32,95%CI:3.60 to 1.04,P<0.001,I2=91.2%).CONCLUSION Lymphocyte count may represent a simple,rapid,and commonly available laboratory index with which to diagnosis infection and predict the severity of CoV infections,including COVID-19.

Haematology Parameters of Apparently Healthy Prospective Whole Blood Donors in a Nigerian Hospital Setting

Background: Adequate selection of a prospective whole blood donor protects his health and safety of the recipient. Objectives: The main objective of this study was to determine the haematology parameters of apparently healthy prospective whole blood donors. Participants and Methods: This was a hospital based prospective study carried out from August to October 2020 at the blood transfusion unit of the Lagos State University Teaching Hospital (LASUTH), Ikeja, Nigeria. A structured pretested questionnaire was used for data collection. The socio demographic status and the haematology parameters of apparently healthy prospective whole blood donors who tested negative for HIV, hepatitis B and C markers were captured. Obtained data were analysed with the statistical package for the social scientist software version 20. Results: One hundred male (97.1%) and three female (2.9%) apparently healthy prospective whole blood donors were studied. The median age of study subjects was 30 years. Obtained median haematology parameter values were 13 g/dl, 40%, 4.9/nl and 203.9/nl for haemoglobin concentration, haematocrit, total white cell and platelet counts respectively. The median values for the mean corpuscular haemoglobin concentration (MCHC), mean corpuscular haemoglobin (MCH) and mean corpuscular volume (MCV) of participants were 32.6 g/dl, 27.7 pg and 85.7 fl respectively. Observed prevalence of subnormal haematology parameters for haemoglobin concentration, total white cells, platelets were 12.6%, 25.2%, and 13.6% respectively. Also subnormal values for MCHC, MCH, MCV were 11.7%, 26.2%, and 16.5% respectively among prospective whole blood donors in this study. No higher than normal haematology parameter values were observed. Median values for erythrocyte sedimentation rate was 8.4 mm/hr. Conclusion: A significant percentage of apparently healthy prospective whole blood donors had subnormal haematology parameters values. Obtained normal values in our study are comparable with local reference range reports from previous studies in Nigeria and other parts of Africa. 124947 .

Comparison of demographic features and laboratory parameters between COVID-19 deceased patients and surviving severe and critically ill cases

BACKGROUND Coronavirus disease 2019(COVID-19) has been far more devastating than expected, showing no signs of slowing down at present. Heilongjiang Province is the most northeastern province of China, and has cold weather for nearly half a year and an annual temperature difference of more than 60℃, which increases the underlying morbidity associated with pulmonary diseases, and thus leads to lung dysfunction. The demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province, China with such climatic characteristics are still not clearly illustrated.AIM To illustrate the demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province by comparing with those of surviving severe and critically ill cases.METHODS COVID-19 deceased patients from different hospitals in Heilongjiang Province were included in this retrospective study and compared their characteristics with those of surviving severe and critically ill cases in the COVID-19 treatment center of the First Affiliated Hospital of Harbin Medical University. The surviving patients were divided into severe group and critically ill group according to the Diagnosis and Treatment of New Coronavirus Pneumonia(the seventh edition).Demographic data were collected and recorded upon admission. Laboratory parameters were obtained from the medical records, and then compared among the groups.RESULTS Twelve COVID-19 deceased patients, 27 severe cases and 26 critically ill cases were enrolled in this retrospective study. No differences in age, gender, and number of comorbidities between groups were found. Neutrophil percentage(NEUT%), platelet(PLT), C-reactive protein(CRP), creatine kinase isoenzyme(CK-MB), serum troponin I(TNI) and brain natriuretic peptides(BNP) showed significant differences among the groups(P = 0.020, P = 0.001, P < 0.001, P = 0.001, P < 0.001, P <0.001, respectively). The increase of CRP, D-dimer and NEUT% levels, as well as the decrease of lymphocyte count(LYMPH) and PLT counts, showed significant correlation with death of COVID-19 patients(P = 0.023, P = 0.008, P = 0.045, P = 0.020, P = 0.015, respectively).CONCLUSION Compared with surviving severe and critically ill cases, no special demographic features of COVID-19 deceased patients were observed, while some laboratory parameters including NEUT%, PLT, CRP, CK-MB, TNI and BNP showed significant differences. COVID-19 deceased patients had higher CRP, D-dimer and NEUT% levels and lower LYMPH and PLT counts.

Approach for Training Quantum Neural Network to Predict Severity of COVID-19 in Patients

Currently,COVID-19 is spreading all over the world and profoundly impacting people’s lives and economic activities.In this paper,a novel approach called the COVID-19 Quantum Neural Network(CQNN)for predicting the severity of COVID-19 in patients is proposed.It consists of two phases:In the first,the most distinct subset of features in a dataset is identified using a Quick Reduct Feature Selection(QRFS)method to improve its classification performance;and,in the second,machine learning is used to train the quantum neural network to classify the risk.It is found that patients’serial blood counts(their numbers of lymphocytes from days 1 to 15 after admission to hospital)are associated with relapse rates and evaluations of COVID-19 infections.Accordingly,the severity of COVID-19 is classified in two categories,serious and non-serious.The experimental results indicate that the proposed CQNN’s prediction approach outperforms those of other classification algorithms and its high accuracy confirms its effectiveness.

Improving 21^stCentury Cancer Therapy: Correcting Treatment Paradoxes and Combining Integrative and Conventional Oncology

Recently there have been many advances in cancer treatment, however, treatment results would be much better if clinical oncologists were educated on the research of cancer metabolism and basic cancer immunology. Many medical oncologists have deficiency in these areas and are devoted to treatment protocols and totally against integrative oncology. One neglected problem is a lack of attention to the cancer patient’s host immunity, which should be evaluated at diagnosis. This huge barrier between integrative and conventional oncology should be eliminated for the benefit of the cancer patient. This communication is an attempt to resolve these important treatment details and bring awareness to this problem.

Predictive value of immune cell counts and neutrophil-to-lymphocyte ratio for 28-day mortality in patients with sepsis caused by intra-abdominal infection

Background:The current study aimed to evaluate the value of immune cell counts and neutrophilto-lymphocyte ratio(NLR)when attempting to predict 28-day mortality.Methods:We conducted an observational retrospective study that included consecutive septic patients.Severity scores on the first day and peripheral circulating immune cell counts(at day 1,day 3,day 5 and day 7 of admission)were collected during each patient’s emergency intensive care unit stay.We assessed the associations of peripheral circulating immune cell counts and NLR with the severity of illness.The relationships between 28-day mortality and peripheral circulating immune cell counts and NLR with were evaluated using Cox proportional cause-specific hazards models.Results:A total of 216 patients diagnosed with sepsis caused by IAI were enrolled.The lymphocyte counts(days 1,3,5 and 7)and monocyte counts(days 3,5 and 7)were significantly lower in nonsurvivors(n=72)than survivors(n=144).The NLR values at each time point were significantly higher in non-survivors.The day 1 lymphocyte counts,as well as the monocyte counts,were significantly lower in the highest-scoring group,when stratified by the Acute Physiology and Chronic Health Evaluation II and Sequential Organ Failure Assessment scores,than in the other groups(p<0.05).The day 1 NLR was significantly higher in the highest-scoring group than in the other groups(p<0.05).The day 5 and day 7 lymphocyte counts,day 3 and day 7 monocyte counts and day 7 NLR were significant predictors of 28-day mortality in the Cox proportional hazards models(day 5 lymphocyte count:hazard ratio,0.123(95%CI,0.055–0.279),p<0.001;day 7 lymphocyte count:hazard ratio,0.115(95%CI,0.052–0.254),p<0.001;day 3 monocyte count:hazard ratio,0.067(95%CI,0.005–0.861),p=0.038;day 7 monocyte count:hazard ratio,0.015(95%CI,0.001–0.158),p<0.001;day 7 NLR:hazard ratio,0.773(95%CI,0.659–0.905),p=0.001).Conclusions The results showed that circulating lymphocytes and monocytes were dramatically decreased within 7 days in non-survivors following sepsis from an IAI.Lymphocyte counts,monocyte counts and NLR appeared to be associated with the severity of illness,and they may serve as independent predictors of 28-day mortality in septic patients with IAIs.

Postoperative Low Absolute Lymphocyte Counts may Predict Poor Outcomes of Hepatocellular Carcinoma After Liver Resection

Background：The absolute lymphocyte counts （ALCs） have been reported as one of worse prognostic factors for hepatocellular carcinoma （HCC） patient after liver transplantation.The aim of this study was to assess the influence of ALCs on the outcomes of patients with hepatitis B virus （HBV）-related HCC within the Milan criteria following liver resection.Methods：Data of patients with HCC within the Milan criteria who received liver resection between January 2007 and June 2013 were reviewed,and perioperative ALCs were carefully monitored.All potential risk factors were statistically analyzed by uni-and multi-variate analyses.The receiver operating characteristic （ROC） curve was used to determine the optimalALCs cut-offvalue to predict HCC recurrence after liver resection.Results：A total of 221 patients were enrolled in the current study.During the follow-up period,106 patients experienced recurrence,and 38 patients died.Multivariate analysis suggested microvascular invasion （MVI）,a tumor grade ≥2,and a low postoperative ALCs in the 1^st postoperative month increased the incidence of postoperative recurrence,besides,MVI,intraoperative transfusion,and a low postoperative ALCs in the 1 st postoperative month were associated with poor overall survival （OS）.An ROC analysis showed that a cut-offvalue of 1.5 × 10^9/L for ALCs in the 1^st postoperative month predicted postoperative recurrence.The 5-year recurrence-free survival （RFS） and OS rates of patients with low postoperative ALCs were 34.5% and 64.8%,respectively,which were significantly lower than those of patients with high postoperative ALC （58.5% for RFS and 86.5% for OS）.Conclusion：Low ALCs in the 1^st postoperative month may be associated with high recurrence incidence and poor OS for patients with HBV-related HCC within the Milan criteria after liver resection.

Clinical outcomes and immune reconstitution in 103 advanced AIDS patients undergoing 12-month highly active antiretroviral therapy

Background Highly active antiretroviral therapy （HAART） produces profound suppression of HIV replication, substantial increase in CD4^＋ T cells, and partial reconstitution of the immune system. However, the numbers of subjects were small in previous Chinese studies. This study evaluated the efficacy and side effects of HAART in Chinese advanced AIDS patients.Methods One hundred and three antiretroviral drug naive AIDS patients were enrolled in this study and were divided into two groups by their baseline CD4^＋ count： 〈 100 cells/μl or ≥ 100 cells/μl. Clinical, virological and immunological outcomes were monitored at baseline and at 1, 3, 6, 9 and 12 months during the course of treatment with HAART.Results One patient died and another was lost from the follow-up. For the remaining 101 HIV/AIDS patients at the 12th month during the HAART, the plasma viral load （VL） was reduced to （3.2±0.7） lg copies/ml, the CD4^＋ count increased to （168 ±51） cells/μl [among which the naive phenotype （CD45RA^＋CD62L^＋） increased to （49 ±27） cells/μl and the memory phenotype （CD45RA^-） increased to （119 ±55） cells/μl], and the percentage of CD4^＋CD28^＋ cells increased. At the same time, there was a significant reduction of CD8^＋ T cell activation. In the 69 patients with the baseline CD4^＋ count 〈100 cells/μl, 37 had a VL 〈50 copies/ml; while in the 34 patients with the baseline CD4^＋ count ≥ 100 cells/μl, 25 had a VL 〈50 copies/ml, the difference between the two groups was statistically significant. The CD4^＋ T cell count showed a two-phase increase during HAART and a significant positive correlation was shown between the change of CD4^＋ count and plasma VL. Over 12 months of HAART, 10 patients had gastrointestinal side effects, 13 peripheral neuritis, 7 hepatic lesions, 8 hematological side effects, 8 skin rashes, 10 lipodystrophy and 1 renal calculus.Conclusions Immune reconstitution as well as the significantly improved clinical outcomes is observed in Chinese advanced AIDS patients after HAART. Side effects are common during HAART and require clinical attention.

Establishment and Evaluation of a Prediction Model of BLR for Severity in Coronavirus Disease 2019

Background:Coronavirus disease 2019(COVID-19)is an emerging infectious disease and has spread worldwide.Clinical risk factors associated with the severity in COVID-19 patients have not yet been well delineated.The aim of this study was to explore the risk factors related with the progression of severe COVID-19 and establish a prediction model for severity in COVID-19 patients.Methods:We retrospectively recruited patients with confirmed COVID-19 admitted in Enze Hospital,Taizhou Enze Medical Center(Group)and Nanjing Drum Tower Hospital between January 24 and March 12,2020.Take the Taizhou cohort as the training set and the Nanjing cohort as the validation set.Severe case was defined based on the World Health Organization Interim Guidance Report criteria for severe pneumonia.The patients were divided into severe and non-severe groups.Epidemiological,laboratory,clinical,and imaging data were recorded with data collection forms from the electronic medical record.The predictive model of severe COVID-19 was constructed,and the efficacy of the predictive model in predicting the risk of severe COVID-19 was analyzed by the receiver operating characteristic curve(ROC).Results:A total of 402 COVID-19 patients were included in the study,including 98 patients in the training set(Nanjing cohort)and 304 patients in the validation set(Nanjing cohort).There were 54 cases(13.43%)in severe group and 348 cases(86.57%)in nonsevere group.Logistic regression analysis showed that bodymassindex(BMI)and lymphocyte count wereindependent risk factors for severe COVID-19(all P<0.05).Logistic regression equation based on risk factors was established as follows:Logit(BL)=–5.552–5.473L+0.418BMI.The area under the ROC curve(AUC)of the training set and the validation set were 0.928 and 0.848,respectively(allP<0.001).The model was simplified to get a new model(BMI and lymphocyte count ratio,BLR)for predicting severe COVID-19 patients,and the AUC in the training set and validation set were 0.926 and 0.828,respectively(all P<0.001).Conclusions:Higher BMI and lower lymphocyte count are critical factors associated with severity of COVID-19 patients.The simplified BLR model has a good predictive value for the severe COVID-19 patients.Metabolic factors involved in the development of COVID-19 need to be further investigated.