‘Protected DNA Probes’ capable of strong hybridization without removal of base protecting groups

We propose a new strategy called the ‘Protected DNA Probes (PDP) method’ in which appropriately protected bases selectively bind to the complementary bases without the removal of their base protecting groups. Previously, we reported that 4-N-acetylcytosine oligonucleotides (ac4C) exhibited a higher hybridization affinity for ssDNA than the unmodified oligonucleotides. For the PDP strategy, we created a modified adenine base and synthesized an N-acylated deoxyadenosine mimic having 6-N-acetyl-8-aza-7-deazaadenine (ac6az8c7A). It was found that PDP containing ac4C and ac6az8c7A exhibited higher affinity for the complementary ssDNA than the corresponding unmodified DNA probes and showed similar base recognition ability. Moreover, it should be noted that this PDP strategy could guarantee highly efficient synthesis of DNA probes on controlled pore glass (CPG) with high purity and thereby could eliminate the time-consuming procedures for isolating DNA probes. This strategy could also avoid undesired base-mediated elimination of DNA probes from CPG under basic conditions such as concentrated ammonia solution prescribed for removal of base protecting groups in the previous standard approach. Here, several successful applications of this strategy to single nucleotide polymorphism detection are also described in detail using PDPs immobilized on glass plates and those prepared on CPG plates, suggesting its potential usefulness

Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

BACKGROUND: Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments. METHODS: Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the primary murine tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient's epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient's primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay. RESULTS: Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model). CONCLUSIONS: These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy

COVID-19 vaccine effectiveness against severe COVID-19 requiring oxygen therapy, invasive mechanical ventilation, and death in Japan: A multicenter case-control study (MOTIVATE study).

INTRODUCTION: Since the SARS-CoV-2 Omicron variant became dominant, assessing COVID-19 vaccine effectiveness (VE) against severe disease using hospitalization as an outcome became more challenging due to incidental infections via admission screening and variable admission criteria, resulting in a wide range of estimates. To address this, the World Health Organization (WHO) guidance recommends the use of outcomes that are more specific to severe pneumonia such as oxygen use and mechanical ventilation. METHODS: A case-control study was conducted in 24 hospitals in Japan for the Delta-dominant period (August-November 2021; "Delta") and early Omicron (BA.1/BA.2)-dominant period (January-June 2022; "Omicron"). Detailed chart review/interviews were conducted in January-May 2023. VE was measured using various outcomes including disease requiring oxygen therapy, disease requiring invasive mechanical ventilation (IMV), death, outcome restricting to "true" severe COVID-19 (where oxygen requirement is due to COVID-19 rather than another condition(s)), and progression from oxygen use to IMV or death among COVID-19 patients. RESULTS: The analysis included 2125 individuals with respiratory failure (1608 cases [75.7%]; 99.2% of vaccinees received mRNA vaccines). During Delta, 2 doses provided high protection for up to 6 months (oxygen requirement: 95.2% [95% CI:88.7-98.0%] [restricted to "true" severe COVID-19: 95.5% {89.3-98.1%}]; IMV: 99.6% [97.3-99.9%]; fatal: 98.6% [92.3-99.7%]). During Omicron, 3 doses provided high protection for up to 6 months (oxygen requirement: 85.5% [68.8-93.3%] ["true" severe COVID-19: 88.1% {73.6-94.7%}]; IMV: 97.9% [85.9-99.7%]; fatal: 99.6% [95.2-99.97]). There was a trend towards higher VE for more severe and specific outcomes. CONCLUSION: Multiple outcomes pointed towards high protection of 2 doses during Delta and 3 doses during Omicron. These results demonstrate the importance of using severe and specific outcomes to accurately measure VE against severe COVID-19, as recommended in WHO guidance in settings of intense transmission as seen during Omicron

On-line Near-Infrared Spectroscopic Sensing Technique for Assessing Milk Quality during Milking

There has been a need in recent years for a method that will enable dairy farmers to assess milk quality of individual cows during milking. We constructed on-line near-infrared (NIR) spectroscopic sensing system on an experimental basis. This system enables NIR spectra of unhomogenized milk to be obtained during milking over a wavelength range of 600 nm to 1050 nm. We also developed calibration models for predicting three major milk constituents (fat, protein and lactose), somatic cell count (SCC) and milk urea nitrogen (MUN) of unhomogenized milk, and we validated the precision and accuracy of the models. The coefficient of determination (r2) and standard error of prediction (SEP) of the validation set for fat were 0.95 and 0.42%, respectively. The values of r2 and SEP for protein were 0.91 and 0.09%, respectively; the values of r2 and SEP for lactose were 0.94 and 0.05%, respectively; the values of r2 and SEP for SCC were 0.82 and 0.27 log SCC/mL, respectively; and the values of r2 and SEP for MUN were 0.90 and 1.33 mg/dL, respectively. These results indicated that the NIR sensing system developed in this study could be used to assess milk quality in real time during milking. The system can provide dairy farmers with information on milk quality and physiological condition of individual cows and therefore give them feedback control for optimizing dairy farm management.Written for presentation at the 2003 ASAE Annual International Meeting Sponsored by ASA

Near-infrared spectroscopic sensing system for on-line milk quality assessment in a milking robot

A near-infrared (NIR) spectroscopic sensing system was constructed on an experimental basis. This system enabled NIR spectra of raw milk to be obtained in an automatic milking system (milking robot system) over a wavelength range of 600 nm to 1050 nm. Calibration models for determining three major milk constituents (fat, protein and lactose), somatic cell count (SCC) and milk urea nitrogen (MUN) of unhomogenized milk were developed, and the precision and accuracy of the models were validated. The coefficient of determination (r2) and standard error of prediction (SEP) of the validation set for fat were 0.95 and 0.25%, respectively. The values of r2 and SEP for lactose were 0.83 and 0.26%, those for protein were 0.72 and 0.15%, those for SCC were 0.68 and 0.28 log SCC/mL, and those for MUN were 0.53 and 1.50 mg/dL, respectively. These results indicate that the NIR spectroscopic system can be used to assess milk quality in real time in an automatic milking system. The system can provide dairy farmers with information on milk quality and physiological condition of an individual cow and, therefore, give them feedback control for optimizing dairy farm management. By using the system, dairy farmers will be able to produce high-quality milk and precision dairy farming will be realized

Near-infrared spectroscopic sensing system for online milk quality assessment in a milking robot

A near-infrared (NIR) spectroscopic sensing system was constructed on an experimental basis. This system enabled NIR spectra of raw milk to be obtained in an automatic milking system (milking robot system) over a wavelength range of 600–1050 nm. Calibration models for determining three major milk constituents (fat, protein and lactose), somatic cell count (SCC) and milk urea nitrogen (MUN) of unhomogenized milk were developed, and the precision and accuracy of the models were validated. The coefficient of determination (r^2) and standard error of prediction (SEP) of the validation set for fat were 0.95 and 0.25%, respectively. The values of r^2 and SEP for lactose were 0.83 and 0.26%, those for protein were 0.72 and 0.15%, those for SCC were 0.68 and 0.28 log SCC/mL, and those for MUN were 0.53 and 1.50 mg/dL, respectively. These results indicate that the NIR spectroscopic system can be used to assess milk quality in real-time in an automatic milking system. The system can provide dairy farmers with information on milk quality and physiological condition of an individual cow and, therefore, give them feedback control for optimizing dairy farm management. By using the system, dairy farmers will be able to produce high-quality milk and precision dairy farming will be realized