Fast kernel-based association testing of non-linear genetic effects for biobank-scale data

Our knowledge of non-linear genetic effects on complex traits remains limited, in part, due to the modest power to detect such effects. While kernel-based tests offer a versatile approach to test for non-linear relationships between sets of genetic variants and traits, current approaches cannot be applied to Biobank-scale datasets containing hundreds of thousands of individuals. We propose, FastKAST, a kernel-based approach that can test for non-linear effects of a set of variants on a quantitative trait. FastKAST provides calibrated hypothesis tests while enabling analysis of Biobank-scale datasets with hundreds of thousands of unrelated individuals from a homogeneous population. We apply FastKAST to 53 quantitative traits measured across ≈ 300 K unrelated white British individuals in the UK Biobank to detect sets of variants with non-linear effects at genome-wide significance.

Predicting food crises using news streams

Anticipating food crisis outbreaks is crucial to efficiently allocate emergency relief and reduce human suffering. However, existing predictive models rely on risk measures that are often delayed, outdated, or incomplete. Using the text of 11.2 million news articles focused on food-insecure countries and published between 1980 and 2020, we leverage recent advances in deep learning to extract high-frequency precursors to food crises that are both interpretable and validated by traditional risk indicators. We demonstrate that over the period from July 2009 to July 2020 and across 21 food-insecure countries, news indicators substantially improve the district-level predictions of food insecurity up to 12 months ahead relative to baseline models that do not include text information. These results could have profound implications on how humanitarian aid gets allocated and open previously unexplored avenues for machine learning to improve decision-making in data-scarce environments.

Drug-food Interactions in the Era of Molecular Big Data, Machine Intelligence, and Personalized Health

The drug-food interaction brings forth changes in the clinical effects of drugs. While favourable interactions bring positive clinical outcomes, unfavourable interactions may lead to toxicity. This article reviews the impact of food intake on drug-food interactions, the clinical effects of drugs, and the effect of drug-food in correlation with diet and precision medicine. Emerging areas in drug-food interactions are the food-genome interface (nutrigenomics) and nutrigenetics. Understanding the molecular basis of food ingredients, including genomic sequencing and pharmacological implications of food molecules, helps to reduce the impact of drug-food interactions. Various strategies are being leveraged to alleviate drug-food interactions; measures including patient engagement, digital health, approaches involving machine intelligence, and big data are a few of them. Furthermore, delineating the molecular communications across dietmicrobiome- drug-food-drug interactions in a pharmacomicrobiome framework may also play a vital role in personalized nutrition. Determining nutrient-gene interactions aids in making nutrition deeply personalized and helps mitigate unwanted drug-food interactions, chronic diseases, and adverse events from their onset. Translational bioinformatics approaches could play an essential role in the next generation of drug-food interaction research. In this landscape review, we discuss important tools, databases, and approaches along with key challenges and opportunities in drug-food interaction and its immediate impact on precision medicine.

Modeling fine-grained spatio-temporal pollution maps with low-cost sensors

The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments' ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities.

Breakthroughs and Applications of Organ-on-a-Chip Technology

Organ-on-a-chip (OOAC) is an emerging technology based on microfluid platforms and in vitro cell culture that has a promising future in the healthcare industry. The numerous advantages of OOAC over conventional systems make it highly popular. The chip is an innovative combination of novel technologies, including lab-on-a-chip, microfluidics, biomaterials, and tissue engineering. This paper begins by analyzing the need for the development of OOAC followed by a brief introduction to the technology. Later sections discuss and review the various types of OOACs and the fabrication materials used. The implementation of artificial intelligence in the system makes it more advanced, thereby helping to provide a more accurate diagnosis as well as convenient data management. We introduce selected OOAC projects, including applications to organ/disease modelling, pharmacology, personalized medicine, and dentistry. Finally, we point out certain challenges that need to be surmounted in order to further develop and upgrade the current systems.

Predictive Modelling of Susceptibility to Substance Abuse, Mortality and Drug-Drug Interactions in Opioid Patients

Objective: Opioids are a class of drugs that are known for their use as pain relievers. They bind to opioid receptors on nerve cells in the brain and the nervous system to mitigate pain. Addiction is one of the chronic and primary adverse events of prolonged usage of opioids. They may also cause psychological disorders, muscle pain, depression, anxiety attacks etc. In this study, we present a collection of predictive models to identify patients at risk of opioid abuse and mortality by using their prescription histories. Also, we discover particularly threatening drug-drug interactions in the context of opioid usage.

Methods and Materials: Using a publicly available dataset from MIMIC-III, two models were trained, Logistic Regression with L2 regularization (baseline) and Extreme Gradient Boosting (enhanced model), to classify the patients of interest into two categories based on their susceptibility to opioid abuse. We’ve also used K-Means clustering, an unsupervised algorithm, to explore drug-drug interactions that might be of concern.

Results: The baseline model for classifying patients susceptible to opioid abuse has an F1 score of 76.64% (accuracy 77.16%) while the enhanced model has an F1 score of 94.45% (accuracy 94.35%). These models can be used as a preliminary step towards inferring the causal effect of opioid usage and can help monitor the prescription practices to minimize the opioid abuse.

Discussion and Conclusion: Results suggest that the enhanced model provides a promising approach in preemptive identification of patients at risk for opioid abuse. By discovering and correlating the patterns contributing to opioid overdose or abuse among a variety of patients, machine learning models can be used as an efficient tool to help uncover the existing gaps and/or fraudulent practices in prescription writing. To quote an example of one such incidental finding, our study discovered that insulin might possibly be interacting with opioids in an unfavourable way leading to complications in diabetic patients. This indicates that diabetic patients under long term opioid usage might need to take increased amounts of insulin to make it more effective. This observation backs up prior research studies done on a similar aspect. To increase the translational value of our work, the predictive models and the associated software code are made available under the MIT License.

High Precision Mammography Lesion Identification From Imprecise Medical Annotations

Breast cancer screening using Mammography serves as the earliest defense against breast cancer, revealing anomalous tissue years before it can be detected through physical screening. Despite the use of high resolution radiography, the presence of densely overlapping patterns challenges the consistency of human-driven diagnosis and drives interest in leveraging state-of-art localization ability of deep convolutional neural networks (DCNN). The growing availability of digitized clinical archives enables the training of deep segmentation models, but training using the most widely available form of coarse hand-drawn annotations works against learning the precise boundary of cancerous tissue in evaluation, while producing results that are more aligned with the annotations rather than the underlying lesions. The expense of collecting high quality pixel-level data in the field of medical science makes this even more difficult. To surmount this fundamental challenge, we propose LatentCADx, a deep learning segmentation model capable of precisely annotating cancer lesions underlying hand-drawn annotations, which we procedurally obtain using joint classification training and a strict segmentation penalty. We demonstrate the capability of LatentCADx on a publicly available dataset of 2,620 Mammogram case files, where LatentCADx obtains classification ROC of 0.97, AP of 0.87, and segmentation AP of 0.75 (IOU = 0.5), giving comparable or better performance than other models. Qualitative and precision evaluation of LatentCADx annotations on validation samples reveals that LatentCADx increases the specificity of segmentations beyond that of existing models trained on hand-drawn annotations, with pixel level specificity reaching a staggering value of 0.90. It also obtains sharp boundary around lesions unlike other methods, reducing the confused pixels in the output by more than 60%.

Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups

OBJECTIVE

Deep significance clustering (DICE) is a self-supervised learning framework. DICE identifies clinically similar and risk-stratified subgroups that neither unsupervised clustering algorithms nor supervised risk prediction algorithms alone are guaranteed to generate.

MATERIALS AND METHODS

Enabled by an optimization process that enforces statistical significance between the outcome and subgroup membership, DICE jointly trains 3 components, representation learning, clustering, and outcome prediction while providing interpretability to the deep representations. DICE also allows unseen patients to be predicted into trained subgroups for population-level risk stratification. We evaluated DICE using electronic health record datasets derived from 2 urban hospitals. Outcomes and patient cohorts used include discharge disposition to home among heart failure (HF) patients and acute kidney injury among COVID-19 (Cov-AKI) patients, respectively.

RESULTS

Compared to baseline approaches including principal component analysis, DICE demonstrated superior performance in the cluster purity metrics: Silhouette score (0.48 for HF, 0.51 for Cov-AKI), Calinski-Harabasz index (212 for HF, 254 for Cov-AKI), and Davies-Bouldin index (0.86 for HF, 0.66 for Cov-AKI), and prediction metric: area under the Receiver operating characteristic (ROC) curve (0.83 for HF, 0.78 for Cov-AKI). Clinical evaluation of DICE-generated subgroups revealed more meaningful distributions of member characteristics across subgroups, and higher risk ratios between subgroups. Furthermore, DICE-generated subgroup membership alone was moderately predictive of outcomes.

DISCUSSION

DICE addresses a gap in current machine learning approaches where predicted risk may not lead directly to actionable clinical steps.

CONCLUSION

DICE demonstrated the potential to apply in heterogeneous populations, where having the same quantitative risk does not equate with having a similar clinical profile.

Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses

Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade.

Quantifying the localized relationship between vector containment activities and dengue incidence in a real-world setting: A spatial and time series modelling analysis based on geo-located data from Pakistan

Increasing urbanization is having a profound effect on infectious disease risk, posing significant challenges for governments to allocate limited resources for their optimal control at a sub-city scale. With recent advances in data collection practices, empirical evidence about the efficacy of highly localized containment and intervention activities, which can lead to optimal deployment of resources, is possible. However, there are several challenges in analyzing data from such real-world observational settings. Using data on 3.9 million instances of seven dengue vector containment activities collected between 2012 and 2017, here we develop and assess two frameworks for understanding how the generation of new dengue cases changes in space and time with respect to application of different types of containment activities. Accounting for the non-random deployment of each containment activity in relation to dengue cases and other types of containment activities, as well as deployment of activities in different epidemiological contexts, results from both frameworks reinforce existing knowledge about the efficacy of containment activities aimed at the adult phase of the mosquito lifecycle. Results show a 10% (95% CI: 1-19%) and 20% reduction (95% CI: 4-34%) reduction in probability of a case occurring in 50 meters and 30 days of cases which had Indoor Residual Spraying (IRS) and fogging performed in the immediate vicinity, respectively, compared to cases of similar epidemiological context and which had no containment in their vicinity. Simultaneously, limitations due to the real-world nature of activity deployment are used to guide recommendations for future deployment of resources during outbreaks as well as data collection practices. Conclusions from this study will enable more robust and comprehensive analyses of localized containment activities in resource-scarce urban settings and lead to improved allocation of resources of government in an outbreak setting.

Clinical improvements following bilateral anterior capsulotomy in treatment-resistant depression

BACKGROUND

The purpose of this study was to evaluate a programme of lesion surgery carried out on patients with treatment-resistant depression (TRD).

METHOD

This was a retrospective study looking at clinical and psychometric data from 45 patients with TRD who had undergone bilateral stereotactic anterior capsulotomy surgery over a period of 15 years, with the approval of the Mental Health Act Commission (37 with unipolar depression and eight with bipolar disorder). The Beck Depression Inventory (BDI) before and after surgery was used as the primary outcome measure. The Montgomery-Asberg Depression Rating Scale was administered and cognitive aspects of executive and memory functions were also examined. We carried out a paired-samples t test on the outcome measures to determine any statistically significant change in the group as a consequence of surgery.

RESULTS

Patients improved on the clinical measure of depression after surgery by -21.20 points on the BDI with a 52% change. There were no significant cognitive changes post-surgery. Six patients were followed up in 2013 by phone interview and reported a generally positive experience. No major surgical complications occurred.

CONCLUSIONS

With the limitations of an uncontrolled, observational study, our data suggest that capsulotomy can be an effective treatment for otherwise TRD. Performance on neuropsychological tests did not deteriorate.

Fine-grained dengue forecasting using telephone triage services

Thousands of lives are lost every year in developing countries for failing to detect epidemics early because of the lack of real-time disease surveillance data. We present results from a large-scale deployment of a telephone triage service as a basis for dengue forecasting in Pakistan. Our system uses statistical analysis of dengue-related phone calls to accurately forecast suspected dengue cases 2 to 3 weeks ahead of time at a subcity level (correlation of up to 0.93). Our system has been operational at scale in Pakistan for the past 3 years and has received more than 300,000 phone calls. The predictions from our system are widely disseminated to public health officials and form a critical part of active government strategies for dengue containment. Our work is the first to demonstrate, with significant empirical evidence, that an accurate, location-specific disease forecasting system can be built using analysis of call volume data from a public health hotline.

WiRE

Many rural areas in developing regions remain largely disconnected from the rest of the world due to low purchasing power and the exorbitant cost of existing connectivity solutions. Wireless Rural Extensions (WiRE) is a low-power rural wireless network architecture that provides inexpensive, self-sustainable, and high-bandwidth connectivity. WiRE relies on a high-bandwidth directional wireless backbone with local distribution networks to provide focused IP coverage. WiRE also provides cellular connectivity using OpenBTS-based GSM microcells. It supports a naming and addressing framework that inter-operates with traditional telecom networks and enables a wide range of mobile services on a common IP framework. The entire name network can be built by integrating a range of off-the-shelf components and existing open source tools.

Practical, distributed channel assignment and routing in dual-radio mesh networks

Realizing the full potential of a multi-radio mesh network involves two main challenges: how to assign channels to radios at each node to minimize interference and how to choose high throughput routing paths in the face of lossy links, variable channel conditions and external load. This paper presents ROMA, a practical, distributed channel assignment and routing protocol that achieves good multi-hop path performance between every node and one or more designated gateway nodes in a dual-radio network. ROMA assigns non-overlapping channels to links along each gateway path to eliminate intra-path interference. ROMA reduces inter-path interference by assigning different channels to paths destined for different gateways whenever possible. Evaluations on a 24-node dual-radio testbed show that ROMA achieves high throughput in a variety of scenarios.

One more bit is enough

Achieving efficient and fair bandwidth allocation while minimizing packet loss in high bandwidth-delay product networks has long been a daunting challenge. Existing end-to-end congestion control ( eg TCP) and traditional congestion notification schemes ( eg TCP+AQM/ECN) have significant limitations in achieving this goal. While the recently proposed XCP protocol addresses this challenge, XCP requires multiple bits to encode the congestion-related information exchanged between routers and end-hosts. Unfortunately, there is no space in the IP header for these bits, and solving this problem involves a non-trivial and time-consuming standardization process.In this paper, we design and implement a simple, low-complexity protocol, called Variable-structure congestion Control Protocol (VCP), that leverages only the existing two ECN bits for network congestion feedback, and yet achieves comparable performance to XCP, ie high utilization, low persistent queue length, negligible packet loss rate, and reasonable fairness. On the downside, VCP converges significantly slower to a fair allocation than XCP. We evaluate the performance of VCP using extensive ns2 simulations over a wide range of network scenarios. To gain insight into the behavior of VCP, we analyze a simple fluid model, and prove a global stability result for the case of a single bottleneck link shared by flows with identical round-trip times.

DCAP

This paper proposes a detection mechanism called DCAP for a network provider to monitor incoming traffic and identify misbehaving flows without having to keep per-flow accounting at any of its routers. Misbehaving flows refer to flows that exceed their stipulated bandwidth limit. Through collaborative aggregate policing at both ingress and egress nodes, DCAP is able to quickly narrow the search to a candidate group that contains the misbehaving flows, and eventually identify the individual culprits. In comparison to per-flow policing, the amount of state maintained at an edge router is reduced from O ( n ) to O (√ n ), where n is the number of admitted flows. Simulation results show that DCAP can successfully detect a majority (64--83%) of the misbehaving flows with almost zero false alarms. Packet losses suffered by innocent flows due to undetected misbehaving activity are insignificant (0.02--0.9%). We also successfully build a prototype that demonstrates how DCAP can be deployed with minimal processing overhead in a soft-QoS architecture.

OverQoS

This paper proposes OverQoS , an architecture for providing Internet QoS using overlay networks. OverQoS empowers third-party providers to offer enhanced network services to their customers using the notion of a controlled loss virtual link (CLVL). The CLVL abstraction bounds the loss-rate experienced by the overlay traffic; OverQoS uses it to provide differential rate allocations, statistical bandwidth and loss assurances, and enables explicit-rate congestion control algorithms.

An investigation of geographic mapping techniques for internet hosts

In this paper, we ask whether it is possible to build an IP address to geographic location mapping service for Internet hosts. Such a service would enable a large and interesting class of location-aware applications. This is a challenging problem because an IP address does not inherently contain an indication of location.We present and evaluate three distinct techniques, collectively referred to as IP2Geo , for determining the geographic location of Internet hosts. The first technique, Geo Track , infers location based on the DNS names of the target host or other nearby network nodes. The second technique, GeoPing , uses network delay measurements from geographically distributed locations to deduce the coordinates of the target host. The third technique, GeoCluster , combines partial (and possibly inaccurate) host-to-location mapping information and BGP prefix information to infer the location of the target host. Using extensive and varied data sets, we evaluate the performance of these techniques and identify fundamental challenges in deducing geographic location from the IP address of an Internet host.

Effects of mutations in potential phosphorylation sites on transcytosis of FcRn

The neonatal Fc receptor, FcRn, transports immunoglobulin G (IgG) across intestinal epithelial cells of suckling rats and mice from the lumenal surface to the serosal surface. In cell culture models FcRn transports IgG bidirectionally, but there are differences in the mechanisms of transport in the two directions. We investigated the effects of mutations in the cytoplasmic domain of FcRn on apical to basolateral and basolateral to apical transport of Fc across rat inner medullary collecting duct (IMCD) cells. Basolateral to apical transport did not depend upon determinants in the cytoplasmic domain. In contrast, an essentially tailless FcRn was markedly impaired in apical to basolateral transport. Using truncation and substitution mutants, we identified serine-313 and serine-319 as phosphorylation sites in the cytoplasmic domain of FcRn expressed in Rat1 fibroblasts. Mutations at Ser-319 did not affect transcytosis across IMCD cells. FcRn-S313A was impaired in apical to basolateral transcytosis to the same extent as tailless FcRn, whereas FcRn-S313D transported at wild-type levels. FcRn-S313A recycled more Fc to the apical medium than the wild-type receptor, suggesting that Ser-313 is required to allow FcRn to be diverted from an apical recycling pathway to a transcytotic pathway.

Phage display selection can differentiate insecticidal activity of soybean cystatins

Plant cysteine proteinase inhibitors (phytocystatins) have been implicated as defensive molecules against Coleopteran and Hemipteran insect pests. Two soybean cystatins, soyacystatin N (scN) and soyacystatin L (scL), have 70% sequence identity but scN is a much more potent inhibitor of papain, vicilin peptidohydrolase and insect gut proteinases. When these cystatins were displayed on phage particles, papain-binding affinity and CPI activity of scN were substantially greater than those of scL, in direct correlation with their relative CPI activity as soluble recombinant proteins. Furthermore, scN substantially delayed cowpea weevil (Callosobruchus maculatus (F.)) growth and development in insect feeding bioassays, whereas scL was essentially inactive as an insecticide. Papain biopanning selection of phage-displayed soyacystatins resulted in a 200-1000-fold greater enrichment for scN relative to scL. These results establish that binding affinity of cystatins can be used in phage display biopanning procedures to select variants with greater insecticidal activity, illustrating the potential of phage display and biopanning selection for directed molecular evolution of biological activity of these plant defensive proteins.

The use of orthognathic surgery to facilitate implant placement: a case report

When attempting to restore an edentulous posterior segment of the mandible into which space the opposing maxillary segment has supererupted, it is necessary to idealize the maxillary occlusal plane to accommodate the final restoration. This report discusses superior repositioning of the posterior segment of the maxilla using rigid fixation with simultaneous implant placement into the corresponding posterior edentulous mandibular space.

Two wound-inducible soybean cysteine proteinase inhibitors have greater insect digestive proteinase inhibitory activities than a constitutive homolog

Diverse functions for three soybean (Glycine max L. Merr.) cysteine proteinase inhibitors (CysPIs) are inferred from unique characteristics of differential regulation of gene expression and inhibitory activities against specific Cys proteinases. Based on northern blot analyses, we found that the expression in leaves of one soybean CysPI gene (L1) was constitutive and the other two (N2 and R1) were induced by wounding or methyl jasmonate treatment. Induction of N2 and R1 transcript levels in leaves occurred coincidentally with increased papain inhibitory activity. Analyses of kinetic data from bacterial recombinant CysPI proteins indicated that soybean CysPIs are noncompetitive inhibitors of papain. The inhibition constants against papain of the CysPIs encoded by the wound and methyl jasmonate-inducible genes (57 and 21 nM for N2 and R1, respectively) were 500 to 1000 times lower than the inhibition constant of L1 (19,000 nM). N2 and R1 had substantially greater inhibitory activities than L1 against gut cysteine proteinases of the third-instar larvae of western corn rootworm and Colorado potato beetle. Cysteine proteinases were the predominant digestive proteolytic enzymes in the guts of these insects at this developmental stage. N2 and R1 were more inhibitory than the epoxide trans-epoxysuccinyl-L-leucylamide-(4-guanidino)butane (E-64) against western corn rootworm gut proteinases (50% inhibition concentration = 50, 200, and 7000 nM for N2, R1, and E-64, respectively). However, N2 and R1 were less effective than E-64 against the gut proteinases of Colorado potato beetle. These results indicate that the wound-inducible soybean CysPIs, N2 and R1, function in host plant defense against insect predation, and that substantial variation in CysPI activity against insect digestive proteinases exists among plant CysPI proteins.

Value of antibiotic therapy in tuberculin-positive children with parenchymal lung lesions

BACKGROUND

A tuberculin-positive child with radiological evidence of a parenchymal lung lesion is likely to be treated for tuberculosis by a physician. However, non-tuberculous microbial infections may also cause parenchymal lung lesions. We tried to distinguish tuberculous from non-tuberculous lung lesions by administering a course of antibiotics.

METHODS

Three hundred and five tuberculin-positive children with parenchymal lung lesions due to pneumonia, bronchiectasis (cylindrical and reversible) and minor fissure opacification were studied at the Tuberculosis Clinic, Institute of Child Health, Madras. Those with more serious forms of tuberculosis like miliary, cavitary and segmental lesions and with grade III and IV undernutrition were excluded. Three weeks of oral antibiotic therapy, with erythromycin (30 mg/kg/day) and chloramphenicol (50 mg/kg/day) for the first two weeks followed by co-trimoxazole (trimethoprim 6 mg/kg/day and sulphamethoxazole 25 mg/kg/day) for the third week, was given. Chest X-rays were taken before and after antibiotic therapy.

RESULTS

Sixty per cent of the children with pneumonia, 57% with bronchiectasis and 62% with minor fissure opacification showed complete radiological clearance.

CONCLUSION

In tuberculin-positive children with parenchymal lung lesions radiological clearance was seen in 60% after three weeks of antibiotic therapy indicating that the parenchymal lung lesions were caused by non-tuberculous organisms. Hence a course of antibiotic therapy in these children may have diagnostic value as well as considerable financial, social and therapeutic implications.

The anti-oxidant activity of turmeric (Curcuma longa)

The turmeric anti-oxidant protein (TAP) had been isolated from the aqueous extract of turmeric. The anti-oxidant principle was found to be a heat stable protein. Trypsin treatment abolished the anti-oxidant activity. The anti-oxidant principle had an absorbance maximum at 280 nm. After gel filtration, the protein showed a 2-fold increase in anti-oxidant activity and showed 2 bands in the SDS-PAGE with approximate molecular weight range of 24,000 Da. The protein showed a concentration-dependent inhibitory effect on the promoter induced lipid peroxidation. A 50% inhibitory activity of lipid peroxidation was observed at a protein concentration of 50 micrograms/ml. Ca(2+)-ATPase of rat brain homogenate was protected to nearly 50% of the initial activity from the lipid peroxidant induced inactivation by this protein. This protection of Ca(2+)-ATPase activity was found to be associated with the prevention of loss of -SH groups.

Myelofibrosis and True Histiocytic Lymphoma

A 60-year-old female presented with a history of progressive shortness of breath, fever, and weight loss of 55 pounds. The work-up consisting of computerized axial tomography (CT) scan of thorax and abdomen, mediastinoscopy, and bilateral bone marrow aspiration and biopsy revealed a large-cell or histiocytic lymphoma involving bone marrow with myelofibrosis. Further immunologic and ultrastructural investigation confirmed the true histiocytic origin of the tumor. The patient was treated with 12 courses of intravenous cyclophosphamide, onconvin, doxorubicin, and prednisone and achieved a complete remission with disappearance of clinical symptoms, normal CT scan of thorax and abdomen, and normal bone marrow with disappearance of myelofibrosis from the same site as the previous bone marrow test. At present the patient is in complete remission. We present this case because of the previously unreported association between histiocytic lymphoma and myelofibrosis, and the unusually good response to chemotherapy and the disappearance of fibrosis from the marrow.