Leptospirosis is a zoonotic infection that is caused by the pathogenic species of Leptospira. Rats are the most important reservoirs of these organisms. Our study aimed to characterize Leptospira isolates from humans and rats and elucidate the Leptospira-rat-human relationship in Luzon, Philippines. Forty strains were isolated from humans and rats. The isolates were confirmed to be Leptospira and pathogenic through rrl- and flaB-PCR, respectively. Around 73% of the isolates were found to be lethal to hamsters. Serotyping showed that there were mainly three predominant leptospiral serogroups in the study areas namely Pyrogenes, Bataviae, and Grippotyphosa. Gyrase B gene sequence analysis showed that all the isolates belonged to Leptospira interrogans. Most had 100% similarity with serovar Manilae (15/40), serovar Losbanos (8/40), and serogroup Grippotyphosa (8/40). Strains from each group had highly identical pulsed-field gel electrophoresis patterns and were further grouped as A (Pyrogenes, 14), B (Bataviae, 8), and C (Grippotyphosa, 10). Results further revealed that similar serotypes were isolated from both humans and rats in the same areas. It is suggested that these three predominant groups with highly similar intra-group PFGE patterns may have been primarily transmitted by rats and persistently caused leptospirosis in humans particularly in the Luzon islands.

Melanoma is considered to be the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in its prognosis. Herein, we developed a transfer learning-based biomarker discovery model that could aid in the diagnosis and prognosis of this disease. After applying it to the ensemble machine learning model, results revealed that the genes found were consistent with those found using other methodologies previously applied to the same TCGA (The Cancer Genome Atlas) data set. Further novel biomarkers were also found. Our ensemble model achieved an AUC of 0.9861, an accuracy of 91.05, and an F1 score of 90.60 using an independent validation data set. This study was able to identify potential genes for diagnostic classification (C7 and GRIK5) and diagnostic and prognostic biomarkers (S100A7, S100A7, KRT14, KRT17, KRT6B, KRTDAP, SERPINB4, TSHR, PVRL4, WFDC5, IL20RB) in melanoma. The results show the utility of a transfer learning approach for biomarker discovery in melanoma.

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The COVID-19 pandemic has burdened the public health system in the Philippines since January 2020. In Western Visayas (Region 6), Philippines, issues have been raised on the limitations of the government’s response on testing, contact tracing, and augmentation of healthcare facilities. Using data from the Western Visayas - Regional Epidemiologic Surveillance Unit (WV - RESU) from March 20 – June 20, 2020, the following observations were made: 1) Of the 6 provinces, Iloilo had the highest % tests done per capita which may be linked to the presence of the only regional COVID-19 testing facility in the province at that time, 2) There were delays in the overall processing times for specimens from Antique and Negros Occidental which may be linked to transport logistics and/or laboratory processing, 3) Contact tracing and testing were de-linked – tracing was adequate (3,420/3,503, 97.63%), but less than 50% of these (1,668/3,420) were tested, 4) Hospital and quarantine facility capacities were still adequate, but their utilization rates needed to be monitored continuously for further augmentation, if needed. This data shows the challenges of establishing a pandemic response in one of the regions in the Philippines.