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Background The detection of Alzheimer’s Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classification of MCI-converts and MCI-non-converts has not been explored profusely and the maximum classification accuracy reported is rather low. Thus, this paper proposes a Machine Learning approach for classifying patients of MCI into two groups one who converted to AD and the others who are not diagnosed with any signs of AD. The proposed algorithm is also used to distinguish MCI patients from controls (CN). This work uses the Structural Magnetic Resonance Imaging data. Methods This work proposes a 3-D variant of Local Binary Pattern (LBP), called LBP-20 for extracting features. The method has been compared with 3D-Discrete Wavelet Transform (3D-DWT). Subsequently, a combination of 3D-DWT and LBP-20 has been used for extracting features. The relevant features are selected using the Fisher Discriminant Ratio (FDR) and finally the classification has been carried out using the Support Vector Machine. Results The combination of 3D-DWT with LBP-20 results in a maximum accuracy of 88.77. Similarly, the proposed combination of methods is also applied to distinguish MCI from CN. The proposed method results in the classification accuracy of 90.31 in this data. Conclusion The proposed combination is able to extract relevant distribution of microstructures from each component, obtained with the use of DWT and thereby improving the classification accuracy. Moreover, the number of features used for classification is significantly less as compared to those obtained by 3D-DWT. The performance of the proposed method is measured in terms of accuracy, specificity and sensitivity and is found superior in comparison to the existing methods. Thus, the proposed method may contribute to effective diagnosis of MCI and may prove advantageous in clinical settings.

Human brain MRI images are complex, and matters present in the brain exhibit non-spherical shape. There exits uncertainty in the overlapping structure of brain tissue, i.e. a lack of distinctness in the class definition. Soft clustering methods can efficiently handle the uncertainty, and plane-based clustering methods are found to be more efficient for non-spherical shape data. Fuzzy k-plane clustering (FkPC) method is a soft plane-based clustering algorithms that can handle the uncertainty in medical images, but its performance degraded in the presence of noise. In this research work, we incorporated local spatial information in the FkPC clustering method to handle the noise present in the image. This spatial regularization term included in the proposed FkPC_S method refines the membership value of noisy pixel with the help of immediate neighbour pixels information. To show the effectiveness of the proposed FkPC_S method, extensive experiments are performed on one synthetic image and two publicly available human brain MRI datasets. The performance of the proposed method is compared with 10 related methods in terms of average segmentation accuracy and dice score. The experiments result shows that the proposed FkPC_S method is superior in comparison with 10 related methods in the presence of noise. Statistically significance difference and superior performance of the proposed method in comparison with other methods are also found using Friedman test.

Spinocerebellar ataxia type 12 (SCA12) is a neurogenetic disease, marked with prominent action tremors in the upper limbs. Neuroimaging techniques like magnetic resonance imaging (MRI) are used by doctors to find the affected areas of SCA12 disease. In literature, most of the research work have used 2D-feature extraction methods, which do not consider pixel information from adjacent slices of the MRI volume, which may be relevant to distinguish healthy from the patient suffering from a particular disease. To overcome the problem of 2D-feature extraction method, we investigate six well-recognized 3D-feature extraction techniques based on varied principles individually and in combination from whole brain gray matter volume. To obtain the optimal set of relevant features, we investigated eight well-known feature selection methods. The support vector machine (SVM) was used as the classifier. Experimental results demonstrate the superior performance of 3D-feature extraction methods in comparison to 2D-feature extraction methods. The features obtained from the combination of feature extraction methods (COFEMS) combined with SVM with Recursive Feature Elimination method achieved maximum classification accuracy of 90% and F1-score of 89.25%. The subset of features so obtained is found statistically relevant and non-redundant. Ranking analysis on both feature extraction and feature selection methods is also carried out.

Performance of texture classification for a given set of texture patterns depends on the choice of feature extraction technique. Integration of features from various feature extraction methods not only eliminates risk of method selection but also brings benefits from the participating methods which play complimentary role among themselves to represent underlying texture pattern. However, it comes at the cost of a large feature vector which may contain redundant features. The presence of such redundant features leads to high computation time, memory requirement and may deteriorate the performance of the classifier. In this research workMonirst phase, a pool of texture features is constructed by integrating features from seven well known feature extraction methods. In the second phase, a few popular feature subset selection techniques are investigated to determine a minimal subset of relevant features from this pool of features. In order to check the efficacy of the proposed approach, performance is evaluated on publically available Brodatz dataset, in terms of classification error. Experimental results demonstrate substantial improvement in classification performance over existing feature extraction techniques. Furthermore, ranking and statistical test also strengthen the results.

In this paper classification of mental task-root Brain-Computer Interfaces (BCI) is being investigated, as those are a dominant area of investigations in BCI and are of utmost interest as these systems can be augmented life of people having severe disabilities. The BCI model's performance is primarily dependent on the size of the feature vector, which is obtained through multiple channels. In the case of mental task classification, the availability of training samples to features are minimal. Very often, feature selection is used to increase the ratio for the mental task classification by getting rid of irrelevant and superfluous features. This paper proposes an approach to select relevant and non-redundant spectral features for the mental task classification. This can be done by using four very known multivariate feature selection methods viz, Bhattacharya's Distance, Ratio of Scatter Matrices, Linear Regression and Minimum Redundancy & Maximum Relevance. This work also deals with a comparative analysis of multivariate and univariate feature selection for mental task classification. After applying the above-stated method, the findings demonstrate substantial improvements in the performance of the learning model for mental task classification. Moreover, the efficacy of the proposed approach is endorsed by carrying out a robust ranking algorithm and Friedman's statistical test for finding the best combinations and comparing different combinations of power spectral density and feature selection methods.

Breast cancer (BC) is the most common type of malignancy which covers almost one-fourth of all the cancers diagnosed in women. Conventionally, chemo-, hormonal-, immune-, surgery, and radiotherapy are the clinically available therapies for BC. However, toxicity and other related adverse effects are still the major challenges. A variety of nano platforms have been reported to overcome these limitations, among them, exosomes provide a versatile platform not only for the diagnosis but also as a delivery vehicle for drugs. Exosomes are biological nanovesicles made up of a lipidic bilayer and known for cell-to-cell communication. Exosomes have been reported to be present in almost all bodily fluids, viz., blood, milk, urine, saliva, pancreatic juice, bile, peritoneal, and cerebrospinal fluid. Such characteristics of exosomes have attracted immense interest in cancer diagnosis and therapy. They can deliver bioactive moieties such as protein, lipids, hydrophilic as well as hydrophobic drugs, various RNAs to both distant and nearby recipient cells as well as have specific biological markers. By considering the growing interest of the scientific community in this field, we comprehensively compiled the information about the biogenesis of exosomes, various isolation methods, the drug loading techniques, and their diverse applications in breast cancer diagnosis and therapy along with ongoing clinical trials which will assist future scientific endeavors in a more organized direction.

The COVID-19 pandemic has disrupted health-care systems, leading to concerns about its subsequent impact on non-COVID disease conditions. The diagnosis and management of cancer is time sensitive and is likely to be substantially affected by these disruptions. We aimed to assess the impact of the COVID-19 pandemic on cancer care in India. We did an ambidirectional cohort study at 41 cancer centres across India that were members of the National Cancer Grid of India to compare provision of oncology services between March 1 and May 31, 2020, with the same time period in 2019. We collected data on new patient registrations, number of patients visiting outpatient clinics, hospital admissions, day care admissions for chemotherapy, minor and major surgeries, patients accessing radiotherapy, diagnostic tests done (pathology reports, CT scans, MRI scans), and palliative care referrals. We also obtained estimates from participating centres on cancer screening, research, and educational activities (teaching of postgraduate students and trainees). We calculated proportional reductions in the provision of oncology services in 2020, compared with 2019. Between March 1 and May 31, 2020, the number of new patients registered decreased from 112 270 to 51 760 (54% reduction), patients who had follow-up visits decreased from 634 745 to 340 984 (46% reduction), hospital admissions decreased from 88 801 to 56 885 (36% reduction), outpatient chemotherapy decreased from 173634 to 109 107 (37% reduction), the number of major surgeries decreased from 17 120 to 8677 (49% reduction), minor surgeries from 18 004 to 8630 (52% reduction), patients accessing radiotherapy from 51 142 to 39 365 (23% reduction), pathological diagnostic tests from 398 373 to 246 616 (38% reduction), number of radiological diagnostic tests from 93 449 to 53 560 (43% reduction), and palliative care referrals from 19 474 to 13 890 (29% reduction). These reductions were even more marked between April and May, 2020. Cancer screening was stopped completely or was functioning at less than 25% of usual capacity at more than 70% of centres during these months. Reductions in the provision of oncology services were higher for centres in tier 1 cities (larger cities) than tier 2 and 3 cities (smaller cities). The COVID-19 pandemic has had considerable impact on the delivery of oncology services in India. The long-term impact of cessation of cancer screening and delayed hospital visits on cancer stage migration and outcomes are likely to be substantial. None. For the Hindi translation of the abstract see Supplementary Materials section.

Colletotrichum falcatum, a hemibiotrophic fungal pathogen, causes one of the major devastating diseases of sugarcane—red rot. C. falcatum secretes a plethora of molecular signatures that might play a crucial role during its interaction with sugarcane. Here, we report the purification and characterization of a novel secreted protein of C. falcatum that elicits defense responses in sugarcane and triggers hypersensitive response (HR) in tobacco. The novel protein purified from the culture filtrate of C. falcatum was identified by MALDI TOF/TOF MS and designated as C. falcatum plant defense-inducing protein 1 (CfPDIP1). Temporal transcriptional profiling showed that the level of CfPDIP1 expression was greater in incompatible interaction than the compatible interaction until 120 h post-inoculation (hpi). EffectorP, an in silico tool, has predicted CfPDIP1 as a potential effector. Functional characterization of full length and two other domain deletional variants (CfPDIP1ΔN1-21 and CfPDIP1ΔN1-45) of recombinant CfPDIP1 proteins has indicated that CfPDIP1ΔN1-21 variant elicited rapid alkalinization and induced a relatively higher production of hydrogen peroxide (H2O2) in sugarcane suspension culture. However, in Nicotiana tabacum, all the three forms of recombinant CfPDIP1 proteins triggered HR along with the induction of H2O2 production and callose deposition. Further characterization using detached leaf bioassay in sugarcane revealed that foliar priming with CfPDIP1∆1-21 has suppressed the extent of lesion development, even though the co-infiltration of CfPDIP1∆1-21 with C. falcatum on unprimed leaves increased the extent of lesion development than control. Besides, the foliar priming has induced systemic expression of major defense-related genes with the concomitant reduction of pathogen biomass and thereby suppression of red rot severity in sugarcane. Comprehensively, the results have suggested that the novel protein, CfPDIP1, has the potential to trigger a multitude of defense responses in sugarcane and tobacco upon priming and might play a potential role during plant-pathogen interactions.

Purpose The present study reports a novel conjugate of gemcitabine (GEM) with bovine serum albumin (BSA) and thereof nanoparticles (GEM-BSA NPs) to potentiate the therapeutic efficacy by altering physicochemical properties, improving cellular uptake and stability of GEM. Methods The synthesized GEM-BSA conjugate was extensively characterized by NMR, FTIR, MALDI-TOF and elemental analysis. Conjugation mediated changes in structural conformation and physicochemical properties were analysed by fluorescence, Raman and CD spectroscopy, DSC and contact angle analysis. Further, BSA nanoparticles were developed from BSA-GEM conjugate and extensively evaluated against in-vitro pancreatic cancer cell lines to explore cellular uptake pathways and therapeutic efficacy. Results Various characterization techniques confirmed covalent conjugation of GEM with BSA. GEM-BSA conjugate was then transformed into NPs via high pressure homogenization technique with particle size 147.2 ± 7.3, PDI 0.16 ± 0.06 and ZP -19.2 ± 1.4. The morphological analysis by SEM and AFM revealed the formation of smooth surface spherical nanoparticles. Cellular uptake studies in MIA PaCa-2 (GEM sensitive) and PANC-1 (GEM resistant) pancreatic cell lines confirmed energy dependent clathrin internalization/endocytosis as a primary mechanism of NPs uptake. In-vitro cytotoxicity studies confirmed the hNTs independent transport of GEM in MIA PaCa-2 and PANC-1 cells. Moreover, DNA damage and annexin-V assay revealed significantly higher apoptosis level in case of cells treated with GEM-BSA NPs as compared to free GEM. Conclusions GEM-BSA NPs were found to potentiate the therapeutic efficacy by altering physicochemical properties, improving cellular uptake and stability of GEM and thus demonstrated promising therapeutic potential over free drug. Graphical Abstract ᅟ