Biological role and regulation of circular RNA as an emerging biomarker and potential therapeutic target for cancer.

Circular RNAs (circRNAs) are a unique family of endogenous RNAs devoid of 3' poly-A tails and 5' end caps. These single-stranded circRNAs, found in the cytoplasm, are synthesized via back-splicing mechanisms, merging introns, exons, or both, resulting in covalently closed circular loops. They are profusely expressed across the eukaryotic transcriptome and offer heightened stability against exonuclease RNase R compared to linear RNA counterparts. This review endeavors to provide a comprehensive overview of circRNAs' characteristics, biogenesis, and mechanisms of action. Furthermore, aimed to shed light on the potential of circRNAs as significant biomarkers in various cancer types. It has been performed an exhaustive literature review, drawing on recent studies and findings related to circRNA characteristics, synthesis, function, evaluation techniques, and their associations with oncogenesis. CircRNAs are intricately associated with tumor progression and development. Their multifaceted roles encompass gene regulation through the sponging of proteins and microRNAs, controlling transcription and splicing, interacting with RNA binding proteins (RBPs), and facilitating gene translation. Due to these varied roles, circRNAs have become a focal point in tumor pathology investigations, given their promising potential as both biomarkers and therapeutic agents. CircRNAs, due to their unique biogenesis and multifunctionality, hold immense promise in the realm of oncology. Their stability, widespread expression, and intricate involvement in gene regulation underscore their prospective utility as reliable biomarkers and therapeutic targets in cancer. As our understanding of circRNAs deepens, advanced techniques for their detection, evaluation, and manipulation will likely emerge. These advancements might catalyze the translation of circRNA-based diagnostics and therapeutics into clinical practice, potentially revolutionizing cancer care and prognosis.

Stepping towards pollen DNA metabarcoding: A breakthrough in forensic sciences.

This review is engaged in determining the capability of plant pollen as a significant source of evidence for the linkage between suspects and crime location in forensic sciences. Research and review articles were collected from Google Scholar, the Web of Science, and PubMed. Articles were searched using specific keywords such as "Forensic Palynology," "Pollen metabarcoding," "Plant forensics," and "Pollen" AND "criminal investigation." Boolean logic was also utilized to narrow the articles to be included in this review article. Through the literature and exploratory research, it has been observed in the current study that with advancements in technology, forensic palynology has found its application in creating an association between the crime scene and suspected individuals to have a link to it, as pollen DNA is a long-lasting investigative tool that can effectively help forensic investigations. Moreover, the literature shows that the DNA of pollen and spores has helped forensic scientists link suspects to crime scenes, and the introduction of pollen DNA metabarcoding tools has eased the efforts of palynologists to analyze pollen DNA. The introduction of DNA metabarcoding techniques to analyze pollen from plants has helped identify the geological locations of the plants and ultimately identify the culprit.

Investigation of mutational spectrum in cytochrome P4501B1 (CYP1B1) as the principal cause of primary congenital glaucoma.

Objective: To identify the genetic variants in the CYP1B1 gene associated with Primary Congenital Glaucoma (PCG) and to predict its pathological effect. Method: A descriptive study was conducted in the time period of nine months (September 2021-May 2022) after the ethical approval was taken from The Children Hospital and Institute of Child Health (CH & ICH). Two milliliters of the blood sample from PCG-affected individuals were collected in EDTA vacutainers and genomic DNA was extracted by a phenol-chloroform method. The semi-quantification of extracted DNA was done by agarose gel electrophoresis. PCR amplification was performed by specific primers of CYP1B1 gene then termination sequencing (di-deoxy) was done to detect the genetic variants. Different bioinformatics tools such as BLAST, Ensembl, Clustal Omega, Polyphen and SIFT were used for the further analysis of mutation causing the disease. Result: A total of 85% of patients were bilaterally affected, while 15% were unilaterally affected. Mutation analysis identified five non related known variants. Two missense mutations (c.355 G/T p.A119S and c.685G/A p.E229K) occurred in 94% patients and intragenic SNP occurred in 29% patients along with the 1% somatic (c.693C/A p.F231L) and stop gained mutation (c.840C/A p.C280*). Conclusion: Genetic analysis in the current study showed that 85% of PCG affected patients were due to the CYP1B1 mutation, and disease heterogeneity might be reduced through genetic counseling.

Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.

An Insight into Primary Congenital Glaucoma.

Glaucoma is the second most prominent cause of impaired vision in the world. Over 60 million individuals are presently affected, and 12 million are sightless as a result. Primary congenital glaucoma (PCG) is a childhood disease that can lead to blindness in newborns and very young children. The rate of occurrence of PCG varies in different communities and across geographical boundaries, and its etiology is unknown. It is caused by genetic structural defects in the trabecular meshwork and makes its appearence in newborns and children no older than three years. PCG is most prevalent in populations with high rates of consanguineous marriages. It is categorized by inappropriate development of the eye's aqueous outflow system, causing increased intraocular pressure (IOP) and leading to swelling of the cornea, epiphora, discomfort or pain, enlargement of the eyeball (buphthalmos), corneal opacity, and optic nerve damage. PCG is classified as an autosomal recessive disorder involving four loci. The main culprit is CYP1B1, at locus GLC3A. PCG is also linked with loci GLC3B and GLC3C; however, their genetic factors have only recently been recognized. The gene LTPB2 at locus GLC3D, plays an important role in tissue healing and cell attachment. Trabeculectomy and gonioscopy are effective treatments for PCG. Additional efforts are essential to provide timely screening of children and, most important, to assign sufficient resources to allow healthcare workers to reduce the rate of avoidable blindness in developing countries.

HSV-1 Infection: Role of Viral Proteins and Cellular Receptors.

The interaction between herpes simplex virus type 1 (HSV-1) and its host starts with the attachment of the virus for entry and spreading into host cells involving viral glycoproteins and host receptors. Once entered, it remains persistent as a latent infection throughout the host's life as it cannot be cleared completely by the immune system. Viral regulatory proteins and host factors determine whether the virus will enter into the acute or latent mode of infection. Acute viral infection is usually asymptomatic and self-limiting whereas latent infection may remain in the trigeminal ganglion of oropharyngeal mucosa, where it can be activated at any time depending upon the stimulus. Host innate and adaptive immune elements play important roles in limiting HSV-1 infection by interfering with viral replication but are unable to remove the virus completely. In this review, we update how the major proteins involved in entry and pathogenesis of viruses and immune responses against infection.

fNIRS-based Neurorobotic Interface for gait rehabilitation.

BACKGROUND: In this paper, a novel functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI) framework for control of prosthetic legs and rehabilitation of patients suffering from locomotive disorders is presented. METHODS: fNIRS signals are used to initiate and stop the gait cycle, while a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control the torques of hip and knee joints for minimization of position error. In the present study, the brain signals of walking intention and rest tasks were acquired from the left hemisphere's primary motor cortex for nine subjects. Thereafter, for removal of motion artifacts and physiological noises, the performances of six different filters (i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass, finite impulse response) were evaluated. Then, six different features were extracted from oxygenated hemoglobin signals, and their different combinations were used for classification. Also, the classification performances of five different classifiers (i.e. k-Nearest Neighbour, quadratic discriminant analysis, linear discriminant analysis (LDA), Naïve Bayes, support vector machine (SVM)) were tested. RESULTS: The classification accuracies obtained from SVM using the hrf were significantly higher (p < 0.01) than those of the other classifier/ filter combinations. Those accuracies were 77.5, 72.5, 68.3, 74.2, 73.3, 80.8, 65, 76.7, and 86.7% for the nine subjects, respectively. CONCLUSION: The control commands generated using the classifiers initiated and stopped the gait cycle of the prosthetic leg, the knee and hip torques of which were controlled using the PD-CTC to minimize the position error. The proposed scheme can be effectively used for neurofeedback training and rehabilitation of lower-limb amputees and paralyzed patients.

In silico analysis of the Val66Met mutation in BDNF protein: implications for psychological stress.

The brain-derived neurotrophic factor (BDNF) involves stress regulation and psychiatric disorders. The Val66Met polymorphism in the BDNF gene has been linked to altered protein function and susceptibility to stress-related conditions. This in silico analysis aimed to predict and analyze the consequences of the Val66Met mutation in the BDNF gene of stressed individuals. Computational techniques, including ab initio, comparative, and I-TASSER modeling, were used to evaluate the functional and stability effects of the Val66Met mutation in BDNF. The accuracy and reliability of the models were validated. Sequence alignment and secondary structure analysis compared amino acid residues and structural components. The phylogenetic analysis assessed the conservation of the mutation site. Functional and stability prediction analyses provided mixed results, suggesting potential effects on protein function and stability. Structural models revealed the importance of BDNF in key biological processes. Sequence alignment analysis showed the conservation of amino acid residues across species. Secondary structure analysis indicated minor differences between the wild-type and mutant forms. Phylogenetic analysis supported the evolutionary conservation of the mutation site. This computational study suggests that the Val66Met mutation in BDNF may have implications for protein stability, structural conformation, and function. Further experimental validation is needed to confirm these findings and elucidate the precise effects of this mutation on stress-related disorders.

Cell-free DNA as a potential diagnostic biomarker in academic stress: A case-control study in young adults.

Background: Stress is a pervasive issue in modern life, affecting both physical and mental health. Identifying biomarkers like cell-free DNA (cfDNA) could provide insights into stress response and help detect individuals at risk for stress-related disorders. Objective: The aim of this study is to investigate the potential use of cfDNA as a diagnostic biomarker in individuals experiencing stress. Methodology: A case-control analysis was conducted using convenient sampling on university participants (N = 285 cases, N = 500 controls) aged 18-24. The study assessed haematological and lipid profile parameters using the Sysmex XP-300TM automated analyzer and an automated biochemistry analyzer, and cfDNA was extracted using a standardized in house developed Phenol-Chloroform protocol and estimated using Agarose Gel Electrophoresis and Nanodrop. Statistical analysis was performed using SPSS ver. 21.0. Results: The results indicated a significant difference between stressed individuals and healthy controls in demographic, haematological and biochemical parameters. Specifically, stressed cases had significantly higher levels of cholesterol, LDL cholesterol, triglycerides, glucose, VLDL cholesterol, and lower levels of HDL compared to healthy controls. Stressed cases also showed significantly elevated levels of circulating cfDNA relative to healthy controls. Conclusion: These findings suggest that cfDNA may have potential as a diagnostic biomarker for stress.

Anti-Diabetic, Anti-Cholinesterase, and Anti-Inflammatory Potential of Plant Derived Extracts and Column Semi-Purified Fractions of Ficus benghalensis.

BACKGROUND: The present study aimed to investigate the in-vitro anti-diabetic, anti-cholinesterase, and anti-inflammatory potential of extracts from different parts of Ficus benghalensis, including leaves, stem, and roots, as well as isolated column fractions (F-B-1 C, F-B-2 C, F-B-3 C, and F-B-4 C). METHODS: The extracts and subsequent fractions were evaluated for their inhibitory activity against key enzymes involved in diabetes [α-glucosidase and α-amylase], neurodegenerative diseases [acetylcholinesterase and butyrylcholinesterase], and inflammation (cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX)). RESULTS: The results showed that F. benghalensis leaf extract exhibited the highest α-glucosidase inhibitory activity (73.84%) and α-amylase inhibitory activity (76.29%) at 1000 µg/mL. The stem extract (65.50%) and F-B-2 C fraction (69.67%) also demonstrated significant α-glucosidase inhibitory activity. In terms of anti-cholinesterase activity, the extracts of roots, leaves, and stem showed promising inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), with half maximal inhibitory concentration (IC50) values ranging from 50.50 to 474.83 µg/mL. The derived fractions (F-B-1 C, F-B-2 C, F-B-3 C, and F-B-4 C) also exhibited notable inhibition of AChE and BChE, with IC50 values from 91.85 to 337.94 µg/mL. Moreover, the F-B-3 C fraction demonstrated the highest COX-2 inhibitory potential (85.72%), followed by F-B-1 C (83.13%), the stem extract (80.85%), and the leaves extract (79.00%). The F-B-1 C fraction showed the highest 5-LOX inhibitory activity (87.63%), while the root extract exhibited the lowest inhibition (73.39%). CONCLUSIONS: The results demonstrated promising bioactivity, suggesting the potential of F. benghalensis as a source of natural compounds with therapeutic applications. Further studies are required to identify and isolate the active components responsible for these effects and to evaluate their in-vivo efficacy and safety.

Avoiding contingent incidents by quadrotors due to one or two propellers failure.

With the increasing impact of drones in our daily lives, safety issues have become a primary concern. In this study, a novel supervisor-based active fault-tolerant (FT) control system is presented for a rotary-wing quadrotor to maintain its pose in 3D space upon losing one or two propellers. Our approach allows the quadrotor to make controlled movements about a primary axis attached to the body-fixed frame. A multi-loop cascaded control architecture is designed to ensure robustness, stability, reference tracking, and safe landing. The altitude control is performed using a proportional-integral-derivative (PID) controller, whereas linear-quadratic-integral (LQI) and model-predictive-control (MPC) have been investigated for reduced attitude control and their performance is compared based on absolute and mean-squared error. The simulation results affirm that the quadrotor remains in a stable region, successfully performs the reference tracking, and ensures a safe landing while counteracting the effects of propeller(s) failures.

Efficacy of cell-free DNA as a diagnostic biomarker in breast cancer patients.

Breast cancer is the most prevalent and leading cause of mortality worldwide among women. Cell-free DNA (cfDNA) analysis is an alternative quantitative approach to conventional methods for cancer diagnosis. The current research project aimed to determine the efficacy of cfDNA as a diagnostic biomarker in breast cancer patients in Pakistan. Eighty-four female breast cancer patients were selected as cases, and 152 healthy females as controls. Immunohistochemistry was performed to identify tumor biomarkers along with clinical profiling. cfDNA was extracted from serum using the phenol-chloroform method. The cfDNA level in the serum was estimated using Agarose Gel Electrophoresis and Nanodrop. SPPS version 25.0 was used to perform statistical analyses. The results showed that the cancer biomarkers were significantly associated with breast cancer. The changes in hematological parameters were insignificant, whereas the biochemical parameter variations between the cases and controls were statistically significant. A significant association of cfDNA level with breast cancer was observed. Further cfDNA levels and cancer biomarkers were not statistically significant. A significant correlation was observed between cfDNA and biochemical parameters, except for creatinine, whereas hematological parameters showed no significant correlation.ROC analysis declared cfDNA as an authentic diagnostic marker for breast cancer. It was concluded that the level of cfDNA is significantly increased in breast cancer patients and can be utilized as a diagnostic biomarker.

Characterization of white matter alterations using diffusion kurtosis imaging in patients with amyotrophic lateral sclerosis.

BACKGROUND: To evaluate the degeneration of the corticospinal tract (CST) and corpus callosum (CC) in patients with motor neuron disease and upper motor neuron (UMN) dysfunction using diffusion kurtosis imaging (DKI). METHODS: Twenty-seven patients and 33 healthy controls underwent magnetic resonance imaging along with clinical and neuropsychological testing. Tractography of diffusion tensor images was performed to extract tracts of the bilateral CST and CC. Group mean differences both across the entire averaged tract and along each tract were assessed, including correlations between diffusion metrics and clinical measures. Tract-based spatial statistics (TBSS) was performed to evaluate the spatial distribution of whole-brain microstructural abnormalities in patients. RESULTS: In comparison to controls, patients had significantly higher mean and radial diffusivity and lower fractional anisotropy (FA), kurtosis anisotropy, mean kurtosis (MK), and radial kurtosis (RK) in the CST and CC (p < .017). Along-the-tract analysis revealed changes concentrated in the posterior limb of the internal capsule, corona radiata, and primary motor cortex (false-discovery rate p < .05). FA of the left CST correlated with disease progression rate, whereas MK of the bilateral CST correlated with UMN burden (p < .01). TBSS results corroborated along-tract analysis findings and additionally revealed reduced RK and MK in the fornix, where diffusion tensor imaging (DTI) changes were absent. CONCLUSION: DKI abnormalities in the CST and CC are present in patients with UMN dysfunction, potentially revealing complementary information to DTI regarding the pathology and microstructural alterations occurring in such patients. DKI shows promise as a potential in vivo biomarker for cerebral degeneration in amyotrophic lateral sclerosis.

Natural compound targeting BDNF V66M variant: insights from in silico docking and molecular analysis.

Brain-Derived Neurotrophic Factor (BDNF) is a neurotrophin gene family gene that encodes proteins vital for the growth, maintenance, and survival of neurons in the nervous system. The study aimed to screen natural compounds against BDNF variant (V66M), which affects memory, cognition, and mood regulation. BDNF variant (V66M) as a target structure was selected, and Vitamin D, Curcumin, Vitamin C, and Quercetin as ligands structures were taken from PubChem database. Multiple tools like AUTODOCK VINA, BIOVIA discovery studio, PyMOL, CB-dock, IMOD server, Swiss ADEMT, and Swiss predict ligands target were used to analyze binding energy, interaction, stability, toxicity, and visualize BDNF-ligand complexes. Compounds Vitamin D3, Curcumin, Vitamin C, and Quercetin with binding energies values of - 5.5, - 6.1, - 4.5, and - 6.7 kj/mol, respectively, were selected. The ligands bind to the active sites of the BDNF variant (V66M) via hydrophobic bonds, hydrogen bonds, and electrostatic interactions. Furthermore, ADMET analysis of the ligands revealed they exhibited sound pharmacokinetic and toxicity profiles. In addition, an MD simulation study showed that the most active ligand bound favorably and dynamically to the target protein, and protein-ligand complex stability was determined. The finding of this research could provide an excellent platform for discovering and rationalizing novel drugs against stress related to BDNF (V66M). Docking, preclinical drug testing and MD simulation results suggest Quercetin as a more potent BDNF variant (V66M) inhibitor and forming a more structurally stable complex.

Immune-related therapeutics: an update on antiviral drugs and vaccines to tackle the COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic rapidly spread globally. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, is a positive-sense single-stranded RNA virus with a reported fatality rate ranging from 1% to 7%, and people with immune-compromised conditions, children, and older adults are particularly vulnerable. Respiratory failure and cytokine storm-induced multiple organ failure are the major causes of death. This article highlights the innate and adaptive immune mechanisms of host cells activated in response to SARS-CoV-2 infection and possible therapeutic approaches against COVID-19. Some potential drugs proven to be effective for other viral diseases are under clinical trials now for use against COVID-19. Examples include inhibitors of RNA-dependent RNA polymerase (remdesivir, favipiravir, ribavirin), viral protein synthesis (ivermectin, lopinavir/ ritonavir), and fusion of the viral membrane with host cells (chloroquine, hydroxychloroquine, nitazoxanide, and umifenovir). This article also presents the intellectual groundwork for the ongoing development of vaccines in preclinical and clinical trials, explaining potential candidates (live attenuated-whole virus vaccines, inactivated vaccines, subunit vaccines, DNAbased vaccines, protein-based vaccines, nanoparticle-based vaccines, virus-like particles and mRNA-based vaccines). Designing and developing an effective vaccine (both prophylactic and therapeutic) would be a long-term solution and the most effective way to eliminate the COVID-19 pandemic.

Hybrid EEG-fNIRS BCI Fusion Using Multi-Resolution Singular Value Decomposition (MSVD).

Brain-computer interface (BCI) multi-modal fusion has the potential to generate multiple commands in a highly reliable manner by alleviating the drawbacks associated with single modality. In the present work, a hybrid EEG-fNIRS BCI system-achieved through a fusion of concurrently recorded electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals-is used to overcome the limitations of uni-modality and to achieve higher tasks classification. Although the hybrid approach enhances the performance of the system, the improvements are still modest due to the lack of availability of computational approaches to fuse the two modalities. To overcome this, a novel approach is proposed using Multi-resolution singular value decomposition (MSVD) to achieve system- and feature-based fusion. The two approaches based up different features set are compared using the KNN and Tree classifiers. The results obtained through multiple datasets show that the proposed approach can effectively fuse both modalities with improvement in the classification accuracy.

Detection, quantification and genotype distribution of HCV patients in Lahore, Pakistan by real-time PCR.

BACKGROUND: Hepatitis C virus (HCV) is considered as "Viral Time Bomb" suggested by the World Health Organization and if it is not treated timely, it will lead towards cirrhosis and hepatocellular carcinoma (HCC). OBJECTIVE: The purpose of the present research is to study possible risk factors, frequent genotypes of HCV and its association with different age groups. METHODS: Suspected blood samples from HCV patients were collected from different hospitals of Lahore, Pakistan. Out of 1000 HCV suspected samples, 920 samples were found HCV positive detected by Anti-HCV ELISA, CobasR. kit. The quantification of HCV load was determined by HCV quantification kit and LINEAR ARRAY KIT (Roche) was used for genotype determination by Real-Time PCR (ABI). Statistical analysis was done by using Microsoft Excel. RESULTS: Out of 920 subjects, 77 subjects (8.4%) were false positive and they were not detected by nested PCR. Three PCR positive samples were untypeable. Genotype 3 was predominant in Lahore which was 83.5%, whereas type 1 and 2 were 5.1% and 0.7% respectively. There were also mixed genotypes detected, 1 and 3 were 0.4%, 2 and 3 were 1.41% and 3 and 4 were 0.2% only. Male were more infected of HCV in the age <40 years and females >40years. CONCLUSION: The major risk factor for HCV transmission is by use of unsterilized razors/blades. It is necessary to spread awareness among the general population of Pakistan about HCV transmission risk factors. Regular physical examination at least once a year is recommended, so that early detection of HCV could be done.

Geographical Variations in Life Histories of House Flies, Musca domestica (Diptera: Muscidae), in Punjab, Pakistan.

Musca domestica Linnaeus is an important public health pest with the ability to adapt to diverse climates. Assessment of variations in biology and life-history traits of insects along geographical gradients is important for a successful management plan in different regions. We investigated life-history traits and life table parameters of M. domestica from six different geographical regions of Punjab, Pakistan: Rahim Yar Khan (RYK), Bahawalpur (BWP), Multan (MTN), Lahore (LHR), Gujrat (GJT), and Murree (MRE). Overall, M. domestica from localities of lower latitude and elevations with higher mean temperatures completed their development faster than those from localities of higher latitude and elevations with lower mean temperatures. The immature developmental time was the longest for the MRE population that was collected from higher latitude and elevation with cooler climate, whereas the shortest for the RYK population from lower latitude with warmer climate. Pupal weights were heavier for the RYK, BWP, and MTN populations, all were from the lowest latitude and elevations with warmer climate, compared with rest of the field populations. Similarly, rate of adult eclosion, fecundity, egg hatching, longevity, and life table parameters such as intrinsic rate of population increase, mean relative growth rate, net reproductive rate, and biotic potential were significantly higher for the RYK, BWP, and MTN populations compared with the GJT, LHR, and MRE populations of M. domestica. The current results will probably be of importance when planning management of M. domestica in different geographical regions of Pakistan.

Predicting breast cancer survivability using fuzzy decision trees for personalized healthcare.

Data analysis systems, intended to assist a physician, are highly desirable to be accurate, human interpretable and balanced, with a degree of confidence associated with final decision. In cancer prognosis, such systems estimate recurrence of disease and predict survival of patient; hence resulting in improved patient management. To develop such a prognostic system, this paper proposes to investigate a hybrid scheme based on fuzzy decision trees, as an efficient alternative to crisp classifiers that are applied independently. Experiments were performed using different combinations of: number of decision tree rules, types of fuzzy membership functions and inference techniques. For this purpose, SEER breast cancer data set (1973-2003), the most comprehensible source of information on cancer incidence in United States, is considered. Performance comparisons suggest that, for cancer prognosis, hybrid fuzzy decision tree classification is more robust and balanced than independently applied crisp classification; moreover it has a potential to adapt for significant performance enhancement.