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Background In the west, survival following treatment of childhood acute lymphoblastic leukaemia (ALL) approaches 90%. Outcomes in India do not exceed 70%. To address this disparity, the Indian Collaborative Childhood Leukaemia group (ICiCLe) developed in 2013 a contemporary treatment protocol for uniform risk-stratified management of first presentation ALL based on cytogenetics and minimal residual disease levels (MRD). A multicentre randomised clinical trial opened in 2016 (ICiCLe-ALL-14) and examines the benefit of randomised interventions to decrease toxicity and improve outcomes. Methods Patients 1–18 years with newly diagnosed ALL are categorised into four risk groups based on presentation features, tumour genetics and treatment response. Standard risk includes young (< 10 years) B cell precursor ALL (BCP-ALL) patients with low presentation leucocyte count (< 50 × 10 9 /L) and no high-risk features. Intermediate risk includes BCP-ALL patients with no high-risk features but are older and have high presentation leucocyte counts and/or bulky disease. High risk includes BCP-ALL patients with any high-risk feature, including high-risk genetics, central nervous system leukaemia, poor prednisolone response at treatment day 8 and high MRD (≥ 0·01%) at the end of induction. Patients with T-lineage ALL constitute the fourth risk group. All patients receive four intensive treatment blocks (induction, consolidation, interim maintenance, delayed intensification) followed by 96 weeks of maintenance. Treatment intensity varies by risk group. Clinical data management is based on a web-based remote data capture system. The first randomisation examines the toxicity impact of a shorter induction schedule of prednisolone (3 vs 5 weeks) in young non-high-risk BCP-ALL. The second randomisation examines the survival benefit of substituting doxorubicin with mitoxantrone in delayed intensification for all patients. Primary outcome measures include event-free survival (overall, by risk groups), sepsis rates in induction (first randomisation) and event-free survival rates following second randomisation. Discussion ICiCLe-ALL-14 is the first multicentre randomised childhood cancer clinical trial in India. The pre-trial phase allowed standardisation of risk-stratification diagnostics and established the feasibility of collaborative practice, uniform treatment, patient enrolment and data capture. Pre-trial observations confirm the impact of risk-stratified therapy in reducing treatment-related deaths and costs. Uniform practice across centres allows patients to access care locally, potentially decreasing financial hardship and dislocation. Trial registration Clinical Trials Registry-India (CTRI) CTRI/2015/12/006434 . Registered on 11 December 2015

Supply chain management is essential for businesses to handle uncertainties, maintain efficiency, and stay competitive. Financial risks can arise from various internal and external sources, impacting different supply chain stages. Companies that effectively manage these risks gain a deeper understanding of their procurement activities and implement strategies to mitigate financial threats. This paper explores financial risk assessment in supply chain management using advanced deep learning techniques on big data. The Adaptive Serial Cascaded Autoencoder (ASCA), combined with Long Short-Term Memory (LSTM) and Multi-Layered Perceptron (MLP), is used to evaluate financial risks. A data transformation process is used to clean and prepare financial data for analysis. Additionally, Sandpiper Galactic Swarm Optimization (SGSO) is employed to optimize the deep learning model’s performance. The SGSO-ASCALSMLP-based financial risk prediction model demonstrated superior accuracy compared to traditional methods. It outperformed GRU (gated recurrent unit)-ASCALSMLP by 3.03%, MLP-ASCALSMLP by 7.22%, AE-LSTM-ASCALSMLP by 10.7%, and AE-LSTM-MLP-ASCALSMLP by 10.9% based on F1-score performance. The SGSO-ASCALSMLP model is highly efficient in predicting financial risks, outperforming conventional prediction techniques and heuristic algorithms, making it a promising approach for enhancing financial risk management in supply chain networks.

Background To evaluate the treatment cost and cost effectiveness of a risk‐stratified therapy to treat pediatric acute lymphoblastic leukemia (ALL) in India. Methods The cost of total treatment duration was calculated for a retrospective cohort of ALL children treated at a tertiary care facility. Children were risk stratified into standard (SR), intermediate (IR) and high (HR) for B‐cell precursor ALL, and T‐ALL. Cost of therapy was obtained from the hospital electronic billing systems and details of outpatient (OP) and inpatient (IP) from electronic medical records. Cost effectiveness was calculated in disability‐adjusted life years. Results One hundred and forty five patients, SR (50), IR (36), HR (39), and T‐ALL (20) were analyzed. Median cost of the entire treatment for SR, IR, HR, and T‐ALL was found to be$3900, $ 5500,$7400, and $ 8700, respectively, with chemotherapy contributing to 25%–35% of total cost. Out‐patient costs were significantly lower for SR (p < 0.0001). OP costs were higher than in‐patient costs for SR and IR, while in‐patient costs were higher in T‐ALL. Costs for non‐therapy admissions were significantly higher in HR and T‐ALL (p < 0.0001), representing over 50% of costs of in‐patient therapy. HR and T‐ALL also had longer durations of non‐therapy admissions. Based on WHO‐CHOICE guidelines, the risk‐stratified approach was very cost effective for all categories of patients. Conclusions Risk‐stratified approach to treat childhood ALL is very cost‐effective for all categories in our setting. The cost for SR and IR patients is significantly reduced through decreased IP admissions for both, chemotherapy and non‐chemotherapy reasons. With the ICiCLe‐ALL‐14 risk stratified approach, ALL treatment cost for low‐risk patients is significantly lower as compared to intermediate and high‐risk groups. Additionally, it is a cost‐effective intervention in a low‐middle income country.

The emergence of MDR bacterial pathogens has directed antibiotic discovery research towards alternative therapies and traditional medicines. Boswellia sacra oleoresin (frankincense) was used to treat bacterial infections in traditional Arabian and Asian healing systems for at least 1000 years. Despite this, B. sacra extracts have not been rigorously tested for inhibitory activity against gastrointestinal pathogens or bacterial triggers of autoimmune diseases. Solvent extracts were prepared from Boswellia sacra oleoresins obtained from three regions near Salalah, Oman. MIC values were quantified against gastrointestinal pathogens and bacterial triggers of selected autoimmune diseases by disc diffusion and broth dilution methods. The antibacterial activity was also evaluated in combination with conventional antibiotics, and the class of interaction was determined by ΣFIC analysis. Isobolograms were used to determine the optimal ratios for synergistic combinations. Toxicity was evaluated by ALA and HDF cell viability bioassays. The phytochemical composition of the volatile components of all extracts was identified by nontargeted GC-MS headspace analysis. All methanolic extracts inhibited the growth of all of the bacteria tested, although the extracts prepared using Najdi oleoresin were generally more potent than the Sahli and Houjari extracts. Combinations of the methanolic B. sacra extracts and conventional antibiotics were significantly more effective in inhibiting the growth of several bacterial pathogens. In total, there were 38 synergistic and 166 additive combinations. Approximately half of the synergistic combinations contained tetracycline. All B. sacra extracts were nontoxic in the ALA and HDF cell viability assays. Nonbiased GC-MS headspace analysis of the methanolic extracts putatively identified a high diversity of monoterpenoids, with particularly high abundances of α-pinene. The antibacterial activity and lack of toxicity of the B. sacra extracts indicate their potential in the treatment and prevention of gastrointestinal and autoimmune diseases. Furthermore, the extracts potentiated the activity of several conventional antibiotics, indicating that they may contain resistance-modifying compounds.

Domain swapping is the process by which identical monomeric proteins exchange structural elements to generate dimers/oligomers. Although engineered domain swapping is a compelling strategy for protein assembly, its application has been limited due to the lack of simple and reliable design approaches. Here, we demonstrate that the hydrophobic five-residue ‘cystatin motif’ (QVVAG) from the domain-swapping protein Stefin B, when engineered into a solvent-exposed, tight surface loop between two β-strands prevents the loop from folding back upon itself, and drives domain swapping in non-domain-swapping proteins. High-resolution structural studies demonstrate that engineering the QVVAG stretch independently into various surface loops of four structurally distinct non-domain-swapping proteins enabled the design of different modes of domain swapping in these proteins, including single, double and open-ended domain swapping. These results suggest that the introduction of the QVVAG motif can be used as a mutational approach for engineering domain swapping in diverse β-hairpin proteins. Designing interfaces that can induce protein-protein interactions is a challenging problem. Here the authors show that a five amino acid sequence known to mediate domain swapping in cystatins can drive oligomerization when grafted onto functionally and structurally unrelated host proteins, providing a simple approach to the design of protein assemblies.

BackgroundRetinoblastoma is the most common intraocular tumour in children. This study aimed to assess the visual outcomes and risk factors leading to visual impairment in children with retinoblastoma in the Indian population.MethodsThis is a single-centre retrospective analysis of all patients with unilateral or bilateral retinoblastoma presenting from the year 2014 to mid-2024.ResultsA total of 426 eyes in 306 patients were included in the study. 240 (78.43%) had unilateral macular tumours, while 45 (14.71%) had bilateral macular tumours. 29 (24.17%) patients were classified as visually impaired, and 22 (18.33%) patients were legally blind. The visual impairment and legal blindness observed in groups D and E (55.18% vs 68.18%) was statistically significant compared with groups A–C, with a p value of <0.05. Visual impairment and legal blindness were noted in only 27.59% and 0.05% of patients without macular tumours. Three patients had motor and speech delay. Out of 120 patients with bilateral tumour, 45 successfully underwent occlusion therapy.ConclusionMacular involvement and late-stage presentation were major predictors of poor vision. This study underscores the importance of early diagnosis and treatment, and improved access to paediatric vision screening and visual rehabilitation.

The present study investigated the degradation of Acid Red 131 (AR131) dye using a combination of ultrasound-induced cavitation, ultraviolet (UV) irradiation, chemical oxidants, and photocatalyst, focusing on the effect of operating parameters. It was established that acidic pH, higher input power, and lower initial concentration resulted in higher degradation. Sulphur-doped titanium dioxide (S-TiO 2 ) synthesized using a novel ultrasound-assisted method showed an optimum dosage of 300 ppm for the AR131 degradation with sulphur to titanium ratio of 2:1. In the combination approach, the optimum dosage of hydrogen peroxide (H 2 O 2 ) and potassium persulfate (KPS) was established as 100 ppm and 400 ppm respectively. The maximum degradation of 90.3% was obtained using a combined approach of US + KPS + UV/S-TiO 2 whereas, a maximum synergetic coefficient of 1.57 was obtained for the approach of US + UV/S-TiO 2 with degradation of 86.96%. It was also elucidated that for combination approaches of US + H 2 O 2 , US + H 2 O 2  + KPS, and US + H 2 O 2  + KPS + UV/S-TiO 2 , the synergetic coefficients were lower than one due to undesirable side reactions and radical scavenging. Scale-up studies performed at 15 times of the laboratory scale volume, elucidated that the maximum degradation was obtained as 58.01% for the approach of US + KPS + UV/S-TiO 2 . Therefore, the approach of US + KPS + UV/S-TiO 2 was elucidated as the most efficient in degrading the AR131 dye at both small and large scale of operation. In terms of synergy, the approach of US + UV/S-TiO 2 was more efficient. Overall, an optimized combination approach was successfully demonstrated for the effective degradation of AR131 dye with synergism and better results at a large scale.

Early detection of tea leaf diseases is crucial for maintaining crop yield and agricultural production. However, manual inspection is a time-consuming and error-prone process, emphasizing the need for automated procedures. Deep learning methods have shown great potential in diagnosing plant leaf diseases. Convolutional Neural Networks (CNNs) outperform traditional deep learning models. However, the performance of these approaches is limited due to computational complexity, feature quality issues, and increasing feature dimensionality. In this study, we propose Res4net-CBAM, a deep convolutional neural network (CNN) specifically designed for tea leaf disease diagnosis, aiming to reduce the model’s complexity and improve disease identification accuracy. The Res4net-CBAM model utilizes a residual block-based Res4net architecture with a network interactive convolutional block attention module (CBAM) to accurately extract complex features associated with different diseases. We conducted extensive experiments to compare the performance of our model with standard CNN models such as AlexNet, VGG16, ResNet50, DenseNet121, and InceptionV3, based on metrics such as accuracy, precision, recall, and F1-score. Our results demonstrate that the Res4net-CBAM model outperforms all other models, achieving an average recognition accuracy of 98.27% on self-acquired tea leaf disease data samples. Specifically, the Res4net-CBAM model achieved an average sensitivity of 98.39%, specificity of 98.26%, precision of 98.35%, and F1-score of 98.37%, while utilizing the Adagrad optimizer with a learning rate of 0.001. Moreover, our model surpasses some recent and existing works in this field, highlighting its effectiveness in diagnosing tea leaf diseases.