A Study on the Expression of BCR-ABL Transcript in Mixed Phenotype Acute Leukemia (MPAL) Cases Using the Reverse Transcriptase Polymerase Reaction Assay (RT-PCR) and its Correlation with Hematological Remission Status Post Initial Induction Therapy

<p><strong>Introduction</strong>: The MPAL comprise 2-5% of all acute leukemia. The present WHO 2008 classification has separated two groups in MPAL based on t(9;22) positivity and MLL rearrangement. <strong>Aims & Objectives</strong>: The aim of the present pilot study is to note the incidence of BCR-ABL transcript in MPAL cases using the RT-PCR assay and to correlate the status with hematological remission post induction. <strong>Materials & Methods</strong>: A total of 10 MPAL cases classified on Flow-cytometry based on the current WHO 2008 criteria were enrolled. In all the cases Bone marrow or peripheral blood sample in EDTA was processed for molecular studies and the RT-PCR reaction carried out using primers specific to the t (9;22) and t(4;11) translocation. The post induction check marrow slides were also reviewed. <strong>Results</strong>: Out of the total 10 MPAL cases, 7/10 (70%) were adult and 3/10 (30%) pediatric cases. A total of 4/10 (40%) cases showed positivity for the t(9;22) transcript and none for t (4;11). Of the 4 positive cases, 3/10(30%) were adult cases and 1/10(10%) pediatric case. The BCR-ABL transcript type in adult cases was b3a2 (p210) in 2/3 (66%) and e1a2 (p190) in 1/3 (33.3%) case. The single pediatric case was positive for b3a2 transcript. <strong>Discussion & Conclusion</strong>: All the 4 positive MPAL cases presented with high TLC and low platelet count (p<0.05). The positive cases also showed hematological remission at post induction check marrow (blasts<5%). This could partly be explained due to good response to the imatinib added to the treatment protocol.</p&gt

A Machine Learning Approach for the Non-Destructive Estimation of Leaf Area in Medicinal Orchid <i>Dendrobium nobile</i> L.

In this study, leaf area prediction models of Dendrobium nobile, were developed through machine learning (ML) techniques including multiple linear regression (MLR), support vector regression (SVR), gradient boosting regression (GBR), and artificial neural networks (ANNs). The best model was tested using the coefficient of determination (R2), mean absolute errors (MAEs), and root mean square errors (RMSEs) and statistically confirmed through average rank (AR). Leaf images were captured through a smartphone and ImageJ was used to calculate the length (L), width (W), and leaf area (LA). Three orders of L, W, and their combinations were taken for model building. Multicollinearity status was checked using Variance Inflation Factor (VIF) and Tolerance (T). A total of 80% of the dataset and the remaining 20% were used for training and validation, respectively. KFold (K = 10) cross-validation checked the model overfit. GBR (R2, MAE and RMSE values ranged at 0.96, (0.82–0.91) and (1.10–1.11) cm2) in the testing phase was the best among the ML models. AR statistically confirms the outperformance of GBR, securing first rank and a frequency of 80% among the top ten ML models. Thus, GBR is the best model imparting its future utilization to estimate leaf area in D. nobile

Hemifacial microsomia with pulmonary hypoplasia

Hemifacial microsomia (HFM) is the second most common facial anomaly after cleft lip and palate. HFM is characterised by facial asymmetry, microtia, preauricular tags, macrostomia and cardiac defects. The majority of cases are sporadic, although autosomal dominant and recessive modes of inheritance have been reported. Here, the case of an 11-month-old boy with HFM and pulmonary hypoplasia, which is a rare association, is described. Pulmonary hypoplasia was detected during investigation of the patient for associated anomalies. He is currently on follow-up in the genetic clinic

A Machine Learning Approach for the Non-Destructive Estimation of Leaf Area in Medicinal Orchid Dendrobium nobile L.

In this study, leaf area prediction models of Dendrobium nobile, were developed through machine learning (ML) techniques including multiple linear regression (MLR), support vector regression (SVR), gradient boosting regression (GBR), and artificial neural networks (ANNs). The best model was tested using the coefficient of determination (R2), mean absolute errors (MAEs), and root mean square errors (RMSEs) and statistically confirmed through average rank (AR). Leaf images were captured through a smartphone and ImageJ was used to calculate the length (L), width (W), and leaf area (LA). Three orders of L, W, and their combinations were taken for model building. Multicollinearity status was checked using Variance Inflation Factor (VIF) and Tolerance (T). A total of 80% of the dataset and the remaining 20% were used for training and validation, respectively. KFold (K = 10) cross-validation checked the model overfit. GBR (R2, MAE and RMSE values ranged at 0.96, (0.82&ndash;0.91) and (1.10&ndash;1.11) cm2) in the testing phase was the best among the ML models. AR statistically confirms the outperformance of GBR, securing first rank and a frequency of 80% among the top ten ML models. Thus, GBR is the best model imparting its future utilization to estimate leaf area in D. nobile

Rubinstein-taybi syndrome: Clinical profile of 11 patients and review of literature

Background: Rubinstein-Taybi syndrome (RSTS) is a rare congenital neurodevelopmental disorder, characterized by postnatal growth deficiency, typical dysmorphic features, broad thumbs and toes, and mental retardation. Very few cases are reported in literature from developing countries. Diagnosis is often delayed due to non-familiarity with the characteristic features of this syndrome. Aims: To report 11 cases of RSTS and to review the current literature. Settings And Design: Retrospective study conducted in genetic and metabolic unit of a tertiary care teaching hospital in north India over a period of 3½ years. Materials And Methods: 11 patients with diagnosis of RSTS were identified, and their case sheets were reviewed. Results: Developmental delay was presenting complaint in 10 patients, and seizure in 1 case. 7 patients had microcephaly (head circumference below −3 SD), and a prominent beaked nose was seen in 9 patients. The intelligence quotient (IQ) varied from 22-62 in 7 patients who had mental retardation. The most notable features in hands were broadness, shortening, and flattening of the distal phalanx of thumbs or great toes. Additionally, we also noted webbing of neck, microphthalmia, and pachygyria (on MRI brain) in 1 patient each. Conclusions: The diagnosis of RSTS is primarily clinical and based on characteristic phenotype that is often combined with a variety of somatic anomalies. An early diagnosis facilitates appropriate genetic counseling and in planning the management

Severe ABO hemolytic disease of fetus and newborn requiring blood exchange transfusion

ABO incompatibility and other alloantibodies have emerged as a significant cause of hemolytic disease of fetus and newborn (HDFN), leading to neonatal morbidity and mortality. We report three cases of severe ABO-HDFN where blood exchange transfusions (ETs) were required in neonates with hyperbilirubinemia. Blood grouping (ABO/RhD) was performed using conventional tube technique. The antibody screen was done using commercial three-cell panel (Bio-Rad ID-Diacell-I-II-III, Switzerland) by gel technique. Direct antiglobulin test (DAT) on neonatal sample and compatibility testing were also done by gel technique. Elution on DAT-positive sample was performed using “heat elution” method. All the three neonates were A RhD positive and were born to O RhD-positive mothers who were negative for antibody screen. Their DAT was positive (2+) and the elution of neonatal red cells yielded a positive reaction with A cells which was suggestive of anti-A antibody. The maternal anti-A (immunoglobulin G) antibody titers were high: 512 and 1024 (in two cases). The total serum bilirubin (mg/dl) of the three neonates was 22, 27, and 25 which came down significantly after they received ETs. Severe ABO-HDFN may occur in neonates born to mother with high titer ABO antibodies which can be effectively managed with ET

Bilateral retinoblastoma presenting as metastases to forearm bones four years after the initial treatment

Osseous metastases from retinoblastoma, the most common ocular malignant neoplasm of childhood, are reported most commonly in the skull and long bones. However, metastases to forearm bones are very rare. Here we present a case of bilateral retinoblastoma with metastases to right forearm bones four years after the initial treatment

Extensively drug resistant tuberculosis in a 7-year-old child with interferon-γ and interleukin-12 deficiency

Defects in interferon-γ axis have been shown to be associated with disseminated mycobacterial disease. A case of a previously healthy HIV seronegative child with disseminated extensively drug resistant Mycobacterium tuberculosis infection presenting as splenic abscesses with lymphadenopathy and progressing to disseminated tuberculosis with absent interferon-γ and interleukin-12 production is reported here