Foxp3+ CD25+CD4+ regulatory T (Treg) cells are crucial for the maintenance of immunological self-tolerance and are abundant in tumors. Most of these cells are chemo-attracted to tumor tissues and suppress anti-tumor responses inside the tumor. Currently, several cancer immunotherapies targeting Treg cells are being clinically tested. Cisplatin is one of the most potent chemotherapy drugs widely used for cancer treatment. While cisplatin is a powerful drug for the treatment of multiple cancers, there are obstacles that limit its use, such as renal dysfunction and the development of cisplatin-resistant cancer cells after its use. To minimize these barriers, combinatorial therapies of cisplatin with other drugs have been developed and have proven to be more effective to treat cancer. In the present study, we evaluated the eff ect of the combination therapy using methyl gallate with cisplatin in EL4 murine lymphoma bearing C57BL/6 mice. The combinatorial therapy of methyl gallate and cisplatin showed stronger anti-cancer eff ects than methyl gallate or cisplatin as single treatments. In Treg cell-depleted mice, however, the eff ect of methyl gallate vanished. It was found that methyl gallate treatment inhibited Treg cell migration into the tumor regardless of cisplatin treatment. Additionally, in both the normal and cisplatin-treated tumor-bearing mice, there was no renal toxicity attributed to methyl gallate treatment. These findings suggest that methyl gallate treatment could be useful as an adjuvant method accompanied with cisplatin therapy.
Animals ; Cisplatin* ; Drug Therapy ; Immunotherapy* ; Lymphoma ; Mice ; T-Lymphocytes, Regulatory*

Background: and Purpose: Voice, reflecting cerebral functions, holds potential for analyzing and understanding brain function, especially in the context of cognitive impairment (CI) and Alzheimer’s disease (AD). This study used voice data to distinguish between normal cognition and CI or Alzheimer’s disease dementia (ADD). Methods: This study enrolled 3 groups of subjects: 1) 52 subjects with subjective cognitive decline; 2) 110 subjects with mild CI; and 3) 59 subjects with ADD. Voice features were extracted using Mel-frequency cepstral coefficients and Chroma. Results: A deep neural network (DNN) model showed promising performance, with an accuracy of roughly 81% in 10 trials in predicting ADD, which increased to an average value of about 82.0%±1.6% when evaluated against unseen test dataset. Conclusions Although results did not demonstrate the level of accuracy necessary for a definitive clinical tool, they provided a compelling proof-of-concept for the potential use of voice data in cognitive status assessment. DNN algorithms using voice offer a promising approach to early detection of AD. They could improve the accuracy and accessibility of diagnosis, ultimately leading to better outcomes for patients.