Roy Sudipta K, Ghosh Bappaditya, Chakraborty Ayan, Hazra Santanu, Goswami Bidyut K, Bhattacharjee Soumen
Cell and Molecular Biology Laboratory, Department of Zoology, University of North Bengal, Darjeeling, West Bengal, India.
Genetics and Molecular Laboratory, Department of Zoology, University of North Bengal, Darjeeling, West Bengal, India.
J Vector Borne Dis. 2025 Apr 1;62(2):218-225. doi: 10.4103/JVBD.JVBD_119_24. Epub 2024 Dec 5.
A hike in dengue cases was recorded in last two years, resulting from both single and multiple-serotypes of dengue virus (DENV) and secondary infections, culminating in significant hospitalizations and deaths in India. This study focuses on evaluating symptomatic and haematological parameters in acute dengue patients of the northern part of West Bengal to predict disease severity early on and to analyze the correlation between circulating DENV serotypes with severity.
Dengue patients (N=540) diagnosed as NS1 positives were categorized into 13.7% severe DHF (N=74) and 86.3% mild DF (N=466) and prediction of risk was done using logistic regression. DENV RNA was isolated from blood, converted to cDNA, and detected/serotyped via RT-qPCR by using DENV specific primers.
Only 14.48% (N=11) patients showed single serotypic (DENV2 or DENV3) infection of dengue. In contrast, multi-serotypic infections (N=65) with the prevalence of DENV-2 and DENV-3 co-infections were found among the dengue patients, affecting severe changes in the most critical haematological parameters such as haematocrit and platelet count. The multivariate binary logistic regression model revealed that only six parameters viz., age (p=0.032), presence of joint pain (p=0.015), Haemoglobin level (p<0.001), total RBC count (p=0.024), total WBC count (p=0.003), lymphocyte% (p=0.019) were found to be significantly associated with the risk of DHF.
Most prevalent DENV-2 and DENV-3 infections significantly impact haematocrit and platelet counts in the study region. Our prediction model, incorporating age, joint pain, haemoglobin, RBC, WBC, and lymphocyte, may effectively predict dengue severity.
过去两年间,印度登革热病例数有所增加,这是由登革热病毒(DENV)的单一和多种血清型以及二次感染导致的,最终造成了大量住院病例和死亡。本研究着重评估西孟加拉邦北部急性登革热患者的症状和血液学参数,以便尽早预测疾病严重程度,并分析循环DENV血清型与严重程度之间的相关性。
将诊断为NS1阳性的登革热患者(N = 540)分为13.7%的严重登革出血热(DHF,N = 74)和86.3%的轻度登革热(DF,N = 466),并使用逻辑回归进行风险预测。从血液中分离出DENV RNA,将其转化为cDNA,并通过使用DENV特异性引物的RT-qPCR进行检测/血清分型。
仅14.48%(N = 11)的患者表现为登革热的单一血清型(DENV2或DENV3)感染。相比之下,在登革热患者中发现了多血清型感染(N = 65),其中DENV-2和DENV-3共同感染最为普遍,这影响了最关键的血液学参数如血细胞比容和血小板计数的严重变化。多变量二元逻辑回归模型显示,只有六个参数,即年龄(p = 0.032)、关节疼痛的存在(p = 0.015)、血红蛋白水平(p < 0.001)、红细胞总数(p = 0.024)、白细胞总数(p = 0.003)、淋巴细胞百分比(p = 0.019)与登革出血热风险显著相关。
在研究区域,最普遍的DENV-2和DENV-3感染对血细胞比容和血小板计数有显著影响。我们纳入年龄、关节疼痛、血红蛋白、红细胞、白细胞和淋巴细胞的预测模型可能有效地预测登革热严重程度。