Insight into the structural requirements of pyrimidine-based phosphodiesterase 10A (PDE10A) inhibitors by multiple validated 3D QSAR approaches

Schizophrenia is a complex disorder of thinking and behaviour (0.3-0.7% of the population is affected). The over-expression of phosphodiesterase 10A (PDE10A) enzyme may be a potential target for schizophrenia and Huntington's disease. Because 3D QSAR analysis is one of the most frequently used modelling techniques, in the present study, five different 3D QSAR tools, namely CoMFA, CoMSIA, kNN-MFA, Open3DQSAR and topomer CoMFA methods, were used on a dataset of pyrimidine-based PDE10A inhibitors. All developed models were validated internally and externally. The non-commercial Open3DQSAR produced the best statistical results amongst 3D QSAR tools. The structural interpretations obtained from different methods were thoroughly analysed and were justified on the basis of information obtained from the crystal structure. Information from one method was mostly validated by the results of other methods and vice versa. In the current work, the use of multiple tools in the same analysis revealed more complete information about the structural requirements of these compounds. On the basis of the observations of the 3D QSAR studies, 12 new compounds were designed for better PDE10A inhibitory activity. The current investigation may help in further designing new PDE10A inhibitors with promising activity.

Question-driven summarization of answers to consumer health questions

Automatic summarization of natural language is a widely studied area in computer science, one that is broadly applicable to anyone who needs to understand large quantities of information. In the medical domain, automatic summarization has the potential to make health information more accessible to people without medical expertise. However, to evaluate the quality of summaries generated by summarization algorithms, researchers first require gold standard, human generated summaries. Unfortunately there is no available data for the purpose of assessing summaries that help consumers of health information answer their questions. To address this issue, we present the MEDIQA-Answer Summarization dataset, the first dataset designed for question-driven, consumer-focused summarization. It contains 156 health questions asked by consumers, answers to these questions, and manually generated summaries of these answers. The dataset's unique structure allows it to be used for at least eight different types of summarization evaluations. We also benchmark the performance of baseline and state-of-the-art deep learning approaches on the dataset, demonstrating how it can be used to evaluate automatically generated summaries.

Hydroxyethylamine derivatives as HIV-1 protease inhibitors: a predictive QSAR modelling study based on Monte Carlo optimization

Application of HIV-1 protease inhibitors (as an anti-HIV regimen) may serve as an attractive strategy for anti-HIV drug development. Several investigations suggest that there is a crucial need to develop a novel protease inhibitor with higher potency and reduced toxicity. Monte Carlo optimized QSAR study was performed on 200 hydroxyethylamine derivatives with antiprotease activity. Twenty-one QSAR models with good statistical qualities were developed from three different splits with various combinations of SMILES and GRAPH based descriptors. The best models from different splits were selected on the basis of statistically validated characteristics of the test set and have the following statistical parameters: r² = 0.806, Q² = 0.788 (split 1); r² = 0.842, Q² = 0.826 (split 2); r² = 0.774, Q² = 0.755 (split 3). The structural attributes obtained from the best models were analysed to understand the structural requirements of the selected series for HIV-1 protease inhibitory activity. On the basis of obtained structural attributes, 11 new compounds were designed, out of which five compounds were found to have better activity than the best active compound in the series.

Sensing lesions in tissues with light

The use of light for probing and imaging of biomedical media offers the promise for development of safe, noninvasive, and inexpensive clinical imaging modalities with diagnostic ability. Various properties of light together with the ways it interacts with biological tissues may provide multiple windows to peer inside body organs. Principles and methods for extraction of information about body functions and lesions that capitalize on temporal, spectral, polarization, and spatial characteristics of transmitted light are briefly outlined. As illustrations of the potential and efficacy of light-based techniques, time-sliced and spectroscopic images of normal and cancerous human breast tissues recorded with a femtosecond Ti:sapphire laser and a broadly tunable Cr:forsterite laser, respectively, are presented.

Autosomal Clonal Monoallelic Expression: Natural or Artifactual?

The prevalence of mitotically heritable clonal random monoallelic expression of autosomal genes (aRME) remains controversial. Specifically, presence of clonal aRME is well supported in vitro but remains elusive in vivo. Here, we provide critical insights into this matter and discuss whether prevalent clonal aRME is natural or artifactual.

Geospatial Distribution and Predictors of Mortality in Hospitalized Patients With COVID-19: A Cohort Study

BACKGROUND

The global coronavirus disease 2019 (COVID-19) pandemic offers the opportunity to assess how hospitals manage the care of hospitalized patients with varying demographics and clinical presentations. The goal of this study was to demonstrate the impact of densely populated residential areas on hospitalization and to identify predictors of length of stay and mortality in hospitalized patients with COVID-19 in one of the hardest hit counties internationally.

METHODS

This was a single-center cohort study of 1325 sequentially hospitalized patients with COVID-19 in New York between March 2, 2020, to May 11, 2020. Geospatial distribution of study patients' residences relative to population density in the region were mapped, and data analysis included hospital length of stay, need and duration of invasive mechanical ventilation (IMV), and mortality. Logistic regression models were constructed to predict discharge dispositions in the remaining active study patients.

RESULTS

The median age of the study cohort (interquartile range [IQR]) was 62 (49-75) years, and more than half were male (57%) with history of hypertension (60%), obesity (41%), and diabetes (42%). Geographic residence of the study patients was disproportionately associated with areas of higher population density (r s = 0.235; P = .004), with noted "hot spots" in the region. Study patients were predominantly hypertensive (MAP > 90 mmHg; 670, 51%) on presentation with lymphopenia (590, 55%), hyponatremia (411, 31%), and kidney dysfunction (estimated glomerular filtration rate < 60 mL/min/1.73 m2; 381, 29%). Of the patients with a disposition (1188/1325), 15% (182/1188) required IMV and 21% (250/1188) developed acute kidney injury. In patients on IMV, the median (IQR) hospital length of stay in survivors (22 [16.5-29.5] days) was significantly longer than that of nonsurvivors (15 [10-23.75] days), but this was not due to prolonged time on the ventilator. The overall mortality in all hospitalized patients was 15%, and in patients receiving IMV it was 48%, which is predicted to minimally rise from 48% to 49% based on logistic regression models constructed to project disposition in the remaining patients on ventilators. Acute kidney injury during hospitalization (odds ratioE, 3.23) was the strongest predictor of mortality in patients requiring IMV.

CONCLUSIONS

This is the first study to collectively utilize the demographics, clinical characteristics, and hospital course of COVID-19 patients to identify predictors of poor outcomes that can be used for resource allocation in future waves of the pandemic.

Structural exploration of hydroxyethylamines as HIV-1 protease inhibitors: new features identified

The current study deals with chemometric modelling strategies (Naïve Bayes classification, hologram-based quantitative structure-activity relationship (HQSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA)) to explore the important features of hydroxylamine derivatives for exerting potent human immunodeficiency virus-1 (HIV-1) protease inhibition. Depending on the statistically validated reliable and robust quantitative structure-activity relationship (QSAR) models, important and crucial structural features have been identified that may be responsible for enhancing the activity profile of these hydroxylamine compounds. Arylsulfonamide function along with methoxy or fluoro substitution is important for enhancing activity. Bulky steric substitution at the sulfonamide nitrogen disfavours activity whereas smaller hydrophobic substitution at the same position is found to be favourable. Apart from the crucial oxazolidinone moiety, pyrrolidine, cyclic urea and methyl ester functions are also responsible for increasing the HIV-1 protease inhibitory profile. Observations derived from these modelling studies may be utilized further in designing promising HIV-1 protease inhibitors of this class.

Identification of structural fingerprints for ABCG2 inhibition by using Monte Carlo optimization, Bayesian classification, and structural and physicochemical interpretation (SPCI) analysis

The human breast cancer resistance protein (BCRP), one of the members of the large ATP binding cassette (ABC) transporter superfamily, is crucial for resistance against chemotherapeutic agents. Currently, it has been emerged as one of the best biological targets for the designing of small molecule drugs capable of eliminating multidrug resistance in breast cancer. In order to gain insights into the relationship between the molecular structure of compounds and the ABCG2 inhibition, a multi-QSAR approach using different methods was performed on a dataset of 294 ABCG2 inhibitors with diverse scaffolds. The best models obtained by different chemometric methods have the following statistical characteristics: Monte Carlo Optimization-based QSAR (sensitivity = 0.905, specificity = 0.6255, accuracy = 0.756, and MCC = 0.545), Bayesian classification model (sensitivity = 0.735, specificity = 0.775, and concordance = 0.757); structural and physicochemical interpretation analysis-random forest method (balance accuracy = 0.750, sensitivity = 0.810, and specificity = 0.700). Additionally, structural fingerprints modulating the ABCG2 inhibitory properties were identified from the best models of each method and also validated with each other. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints modulating ABCG2 inhibition.

Insight into structural features of phenyltetrazole derivatives as ABCG2 inhibitors for the treatment of multidrug resistance in cancer

ABCG2 is the principal ABC transporter involved in the multidrug resistance of breast cancer. Looking at the current demand in the development of ABCG2 inhibitors for the treatment of multidrug-resistant cancer, we have explored structural requirements of phenyltetrazole derivatives for ABCG2 inhibition by combining classical QSAR, Bayesian classification modelling and molecular docking studies. For classical QSAR, structural descriptors were calculated from the free software tool PaDEL-descriptor. Stepwise multiple linear regression (SMLR) was used for model generation. A statistically significant model was generated and validated with different parameters (For training set: r = 0.825; Q² = 0.570 and for test set: r = 0.894, r²_pred = 0.783). The predicted model was found to satisfy the Golbraikh and Trospha criteria for model acceptability. Bayesian classification modelling was also performed (ROC scores were 0.722 and 0.767 for the training and test sets, respectively). Finally, the binding interactions of phenyltetrazole type inhibitor with the ABCG2 receptor were mapped with the help of molecular docking study. The result of the docking analysis is aligned with the classical QSAR and Bayesian classification studies. The combined modelling study will guide the medicinal chemists to act faster in the drug discovery of ABCG2 inhibitors for the management of resistant breast cancer.

Time-resolved ring structure of circularly polarized beams backscattered from forward scattering media

The backscattering of circularly polarized light at normal incidence to a half-space of scattering particles is studied using the Electric Field Monte Carlo (EMC) method. The spatial distribution of the backscattered light intensity is examined for both the time-resolved and continuous wave cases for large particles with anisotropy factor, g, in the range 0.8 to 0.97. For the time-resolved case, the backscattered light with the same helicity as that of the incident beam (co-polarized) is found to form a ring centered on the point of incidence. The ring expands and simultaneously grows weak as time increases. The intensity of backscattered light with helicity opposite to that of the incident beam (cross-polarized) is found to exhibit a ring behavior for g >/= 0.85, with significant backscattering at the point of incidence. For the continuous-wave case no such ring pattern is observed in backscattered light for either helicity. The present EMC study suggests that the ring behavior can only be observed in the time domain, in contrast to previous studies of light backscattered from forward scattering media based on the scalar time-independent Fokker-Planck approximation to the radiative transfer equation. The time-dependent ring structure of backscattered light may have potential use in subsurface imaging applications.

Question-driven summarization of answers to consumer health questions

Automatic summarization of natural language is a widely studied area in computer science, one that is broadly applicable to anyone who needs to understand large quantities of information. In the medical domain, automatic summarization has the potential to make health information more accessible to people without medical expertise. However, to evaluate the quality of summaries generated by summarization algorithms, researchers first require gold standard, human generated summaries. Unfortunately there is no available data for the purpose of assessing summaries that help consumers of health information answer their questions. To address this issue, we present the MEDIQA-Answer Summarization dataset, the first dataset designed for question-driven, consumer-focused summarization. It contains 156 health questions asked by consumers, answers to these questions, and manually generated summaries of these answers. The dataset's unique structure allows it to be used for at least eight different types of summarization evaluations. We also benchmark the performance of baseline and state-of-the-art deep learning approaches on the dataset, demonstrating how it can be used to evaluate automatically generated summaries.

First molecular modelling report on tri-substituted pyrazolines as phosphodiesterase 5 (PDE5) inhibitors through classical and machine learning based multi-QSAR analysis

Phosphodiesterase 5 (PDE5) falls under a broad category of metallohydrolase enzymes responsible for the catalysis of the phosphodiesterase bond, and thus it can terminate the action of cyclic guanosine monophosphate (cGMP). Overexpression of this enzyme leads to development of a number of pathological conditions. Thus, targeting the enzyme to develop inhibitors could be useful for the treatment of erectile dysfunction as well as pulmonary hypertension. In the current study, several molecular modelling techniques were utilized including Bayesian classification, single tree and forest tree recursive partitioning, and genetic function approximation to identify crucial structural fingerprints important for optimization of tri-substituted pyrazoline derivatives as PDE5 inhibitors. Later, various machine learning models were also developed that could be utilized to predict and screen PDE5 inhibitors in the future.

Insight into the structural requirements of gelatinases (MMP-2 and MMP-9) inhibitors by multiple validated molecular modelling approaches: Part II

Inhibition of the matrix metalloproteinases (MMPs) is effective against metastasis of secondary tumours. Previous MMP inhibitors have failed in clinical trials due to their off-target toxicity in solid tumours. Thus, newer MMP inhibitors now have paramount importance. Here, different molecular modelling techniques were applied on a dataset of 110 gelatinase (MMP-2 and MMP-9) inhibitors. The objectives of the present study were to identify structural fingerprints for gelatinase inhibition and also to develop statistically validated QSAR models for the screening and prediction of different derivatives as MMP-2 (gelatinase A) and MMP-9 (gelatinase B) inhibitors. The Bayesian classification study provided the ROC values for the training set of 0.837 and 0.815 for MMP-2 and MMP-9, respectively. The linear model also produced the leave-one-out cross-validated Q² of 0.805 (eq. 1, MMP-2) and 0.724 (eq. 2, MMP-9), an r² of 0.845 (eq. 1, MMP-2) and 0.782 (eq. 2, MMP-9), an r²_Pred of 0.806 (eq. 1, MMP-2) and 0.732 (eq. 2, MMP-9). Similarly, non-linear learning models were also statistically significant and reliable. Overall, this study may help in the rational design of newer compounds with higher gelatinase inhibition to fight against both primary and secondary cancers in future.

Greek Warrior Helmet Facies (Wolf-hirschhorn Syndrome)

Wolf-Hirschhorn syndrome (WHS) is caused by a chromosomal deletion of the band 4p16.3 with characteristic craniofacial features -’Greek warrior helmet’5 facies (prominent glabella, hypertelorism, broad beaked nose and frontal bossing), high-arched eyebrows, protruding eyes, epicanthal folds, short philtrum, distinct mouth with downturned corners, micrognathia, dysplastic ears, preauricular tags. Till date there are very few case reports of Wolf-Hirschhorn syndrome.Here we report a case that had characteristic dysmorphic facies (Figure 1) ‘Greek warrior helmet’ and was diagnosed as a case of WHS. But presence of Meningo-encephalocele and lissencephaly is rarely reported in literature in association with Wolf-hirschhorn syndrome till date. J Nepal Paediatr Soc 2014;34(3):239-243 DOI: http://dx.doi.org/10.3126/jnps.v34i3.10289

Exploring molecular fragments for fraction unbound in human plasma of chemicals: a fragment-based cheminformatics approach

Fraction unbound in plasma (fu,p) of drugs is an significant factor for drug delivery and other biological incidences related to the pharmacokinetic behaviours of drugs. Exploration of different molecular fragments for fu,p of different small molecules/agents can facilitate in identification of suitable candidates in the preliminary stage of drug discovery. Different researchers have implemented strategies to build several prediction models for fu,p of different drugs. However, these studies did not focus on the identification of responsible molecular fragments to determine the fraction unbound in plasma. In the current work, we tried to focus on the development of robust classification-based QSAR models and evaluated these models with multiple statistical metrics to identify essential molecular fragments/structural attributes for fractions unbound in plasma. The study unequivocally suggests various N-containing aromatic rings and aliphatic groups have positive influences and sulphur-containing thiadiazole rings have negative influences for the fu,p values. The molecular fragments may help for the assessment of the fu,p values of different small molecules/drugs in a speedy way in comparison to experiment-based in vivo and in vitro studies.

Knowledge of Breastfeeding Practices Among Mothers Attending a Tertiary Care Setting in East India

Background Human milk offers a neonate a balanced diet for healthy growth and development, in addition to its myriad of benefits like preventing stunting, protecting against infectious and chronic diseases, and decreasing infant mortality. Objective To assess the knowledge of mothers and other factors that contribute to breastfeeding practices. Methods This is a one-year hospital-based cross-sectional study that included 400 mothers who followed up with the hospital for the healthcare of their child, aged between six and 24 months. A survey was used for data collection. Results Ninety-three percent of the mothers were from the countryside, and 78% of them were under 25 years of age. Eighty-seven percent of mothers worked at home, while 83% of mothers were part of nuclear households. Ninety-nine percent of mothers delivered their neonates at a medical facility, and 77% of mothers did so for the first time. Only 53% of mothers resorted to exclusive breastfeeding (EBF), even though 68% of mothers were aware of its significance. Thirty-six percent of mothers adopted EBF, while only 23% of women were aware that breastfeeding should be started within the first hour of childbirth. Working women (p=0.000), mothers with several children (p=0.000), mothers older than 25 years of age (p=0.002), and mothers with higher education levels than the 10th grade (p=0.000) showed good understanding and practice of breastfeeding, which was statistically significant (p<0.5). Conclusion The levels of breastfeeding awareness and practice among mothers fell short of both national statistics and WHO recommendations. All helpful information about breastfeeding should be shared with the community at large to improve the data currently available.

Fragment-based QSAR study to explore the structural requirements of DPP-4 inhibitors: a stepping stone towards better type 2 diabetes mellitus management

Dipeptidyl peptidase-4 (DPP-4) inhibitors belong to a prominent group of pharmaceutical agents that are used in the governance of type 2 diabetes mellitus (T2DM). They exert their antidiabetic effects by inhibiting the incretin hormones like glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide which, play a pivotal role in the regulation of blood glucose homoeostasis in our body. DPP-4 inhibitors have emerged as an important class of oral antidiabetic drugs for the treatment of T2DM. Surprisingly, only a few 2D-QSAR studies have been reported on DPP-4 inhibitors. Here, fragment-based QSAR (Laplacian-modified Bayesian modelling and Recursive partitioning (RP) approaches have been utilized on a dataset of 108 DPP-4 inhibitors to achieve a deeper understanding of the association among their molecular structures. The Bayesian analysis demonstrated satisfactory ROC values for the training as well as the test sets. Meanwhile, the RP analysis resulted in decision tree 3 with 2 leaves (Tree 3: 2 leaves). This present study is an effort to get an insight into the pivotal fragments modulating DPP-4 inhibition.

Scrub typhus meningoencephalitis in children: an experience from Eastern India

Scrub typhus meningoencephalitis (STME) is an uncommon but fatal complication of scrub typhus that requires extra diligence in early identification and treatment. The goal of this study was to look at the clinical characteristics, laboratory results, and outcome of STME in children. A retrospective study was conducted in the paediatric intensive care unit of a tertiary care hospital in Eastern India between April 2021 and September 2022. It was carried out in children aged 1 month-12 years. Sixteen children were diagnosed with STME out of 75 acute encephalitis syndrome (AES) cases. The male-to-female ratio was 3:1, with a mean age of 4.28 ± 3.36 years. All the children hailed from rural areas. Fever (100%), convulsions (87.5), altered sensorium (93.75%), nuchal stiffness (25%), vomiting (75%), pallor (75%), and hepatomegaly (50%) were the most prevalent clinical manifestations. The average duration of fever upon presentation was 8.25 ± 2.11 days and the average length of hospital stay was 9.00 ± 4.59 days. The complications encountered were shock (3), interstitial pneumonia (1), myocarditis (1), raised intracranial pressure (1), and right-sided hemiplegia (1). Fifteen children recovered completely, whereas one child suffered from residual right-sided neurodeficit. A high index of suspicion needs to be maintained for STME as a possible AES entity, as anti-scrub measures administered promptly can go a long way in mitigating the related morbidity and mortality especially in resource-constrained settings.

Exploring different classification-dependent QSAR modelling strategies for HDAC3 inhibitors in search of meaningful structural contributors

Histone deacetylase 3 (HDAC3), a Zn2+-dependent class I HDACs, contributes to numerous disorders such as neurodegenerative disorders, diabetes, cardiovascular disease, kidney disease and several types of cancers. Therefore, the development of novel and selective HDAC3 inhibitors might be promising to combat such diseases. Here, different classification-based molecular modelling studies such as Bayesian classification, recursive partitioning (RP), SARpy and linear discriminant analysis (LDA) were conducted on a set of HDAC3 inhibitors to pinpoint essential structural requirements contributing to HDAC3 inhibition followed by molecular docking study and molecular dynamics (MD) simulation analyses. The current study revealed the importance of hydroxamate function for Zn2+ chelation as well as hydrogen bonding interaction with Tyr298 residue. The importance of hydroxamate function for higher HDAC3 inhibition was noticed in the case of Bayesian classification, recursive partitioning and SARpy models. Also, the importance of substituted thiazole ring was revealed, whereas the presence of linear alkyl groups with carboxylic acid function, any type of ester function, benzodiazepine moiety and methoxy group in the molecular structure can be detrimental to HDAC3 inhibition. Therefore, this study can aid in the design and discovery of effective novel HDAC3 inhibitors in the future.

Effects of Porting Essie Tokenization and Normalization to Solr

Search for information is now an integral part of healthcare. Searches are enabled by search engines whose objective is to efficiently retrieve the relevant information for the user query. When it comes to retrieving biomedical text and literature, Essie search engine developed at the National Library of Medicine (NLM) performs exceptionally well. However, Essie is a software system developed for NLM that has ceased development and support. On the other hand, Solr is a popular opensource enterprise search engine used by many of the world's largest internet sites, offering continuous developments and improvements along with the state-of-the-art features. In this paper, we present our approach to porting the key features of Essie and developing custom components to be used in Solr. We demonstrate the effectiveness of the added components on three benchmark biomedical datasets. The custom components may aid the community in improving search methods for biomedical text retrieval.

Geometric and Dosimetric Evaluation of a RayStation Deep Learning Model for Auto-Segmentation of Organs at Risk in a Real-World Head and Neck Cancer Dataset

AIMS

To assess geometric accuracy and dosimetric impact of a deep learning segmentation (DLS) model on a large, diverse dataset of head and neck cancer (HNC) patients treated with intensity-modulated proton therapy (IMPT).

MATERIALS AND METHODS

A 3D U-Net-based DLS model was applied to CT datasets of 124 HNC patients treated with IMPT at 50.4-70.0 GyRBE. Thirty organs-at-risk (OARs), delineated manually (GT-OARs) were analysed for similarity metrics with auto-segmented OARs, without (DLS-nonedited) and with (DLS-edited) manual correction, using volume, Dice similarity coefficient (DSC), and Hausdorff distance (HD). Dosimetric impact of auto-segmentation error was assessed as absolute dose difference of mean (ΔDmean) and maximum (ΔDmax).

RESULTS

The cohort includes patients with postoperative (47.6%), flap reconstruction (12.1%), mouth bites (79.8%), dental implants (54.8%), and surgical implants (3.2%). DLS failed in 11 patients with significant anatomical challenges and artifact. Compared with GT-OARs, DLS-nonedited under-segmented 11/12 Gr-A (central nervous system, arteries, bone) (p < 0.05) and over-segmented 13/18 Gr-B (glandular, digestive, airways) OARs. DSC scores were good (>0.8), intermediate (0.6-0.8), intermediate-poor (0.5-0.6), and poor (<0.5) in 12, 6, 4, and 8 OARs. HD were good (<4mm), intermediate (4-6mm), poor (6-8mm), and very poor (>8mm) in 5, 7, 4, and 14 OARs. Compared with manually corrected, DLS-edited OARs, all DLS-nonedited OARs demonstrated excellent similarity with DSC>0.8 and HD<4mm. On average, auto-segmentation took 2.51 minutes, while correction took 6.24 minutes. The mean values of ΔDmean and ΔDmax were within ±300 and ±3 cGyRBE, except for oesophagus and larynx, where the mean ΔDmean increases up to 837.14 cGyRBE.

CONCLUSION

Patient posture, nonbiological materials, and anatomical deformities influence DLS accuracy. The model's overall performance is adequate and efficient with skilled manual editing needed for few OARs.

Filariasis presenting as pancytopenia in a child

A female child hailing from South Asia, India presented with pallor, multiple petechiae and ecchymosis. Based on the clinical picture and demography, the differentials considered were pancytopenia of nutritional origin, acute leukaemia, autoimmune and infective aetiologies. After ruling these out by respective tests, a literature review was done which revealed the possibility of filariasis especially in a patient with eosinophilia which was present in our case. A repeat peripheral blood smear study with a nocturnally drawn sample revealed multiple microfilariae and a diagnosis of filariasis was made. The patient was treated with triple drug therapy of diethylcarbamazine (6 mg/kg), ivermectin (6 µg/kg) and albendazole (400 mg) administered as a single dose. Subsequent haemograms showed improved cell counts. This along with a previous handful of case reports emphasises filariasis as one of the differentials of pancytopenia and should be kept in mind while evaluating for the same, especially in the endemic areas.