Meaning and Significance of “Alkalization Therapy for Cancer”

Objectives of the StudyOur research aims to answer the following questions. Can cancer progression be stopped by changing the body condition of person with cancer? Can cancer be cured?If cancer progression can be stopped, what is the underlying mechanism?Theoretical Rationale for Alkalization TherapyAlmost 70 years ago, Goldblatt H. & Cameron G. reported on the idea of alkalization therapy. Before that, Otto Warburg had been studying the metabolism of cancer and had discovered the essential nature of cancer. He published a review in Science in 1956 under the title “On the origin of cancer cells”. From his phenomena described above, we established the theoretical rationale for alkalization therapy, based on the question of “How does cancer form and what is its nature”?Limitations of Deductive Methods and Inductive ApproachesIn this paper, we describe a method to reconstruct the limitations and weaknesses of modern cancer medicine as Science-based Medicine using an inductive method, and to present a new vision of cancer therapy. How should we treat cancer? (Case presentation): Using a specific clinical case, we present patients in whom were successfully treated with no or few anticancer drugs.SummaryThe biggest weakness of current cancer treatments is that they only treat the cancer and not the actual patient. The “alkalization therapy” that we advocate does not compete with any of the current standard treatments, but improves the effectiveness of standard treatments, reduces side effects, and lowers medical costs

Effects of alkalization therapy on hepatocellular carcinoma: a retrospective study

BackgroundIn hepatocellular carcinoma (HCC) patients, is difficult to prevent recurrence even when remission is achieved. In addition, even with the advent of drugs that are effective for the treatment of HCC, a satisfactory extension of patient survival has not been achieved. To overcome this situation, we hypothesized that the combination of alkalization therapy with standard treatments will improve the prognosis of HCC. We here report the clinical results of HCC patients treated with alkalization therapy at our clinic.Patients and methodsPatients with HCC treated at Karasuma Wada Clinic (in Kyoto, Japan), from January 1, 2013, to December 31, 2020 were analyzed. Overall survival (OS) from both the time of diagnosis and the start of alkalization therapy for each patient was compared. The mean urine pH was also calculated as a surrogate marker of tumor microenvironment pH, and OS from the start of alkalization therapy was compared between patients with a mean urine pH of ≥ 7.0 and those with a mean urine pH of < 7.0.ResultsTwenty-three men and six women were included in the analysis, with a mean age at diagnosis of 64.1 years (range: 37–87 years). Seven of the 29 patients had extrahepatic metastases. Patients were divided into two groups according to their mean urine pH after the initiation of alkalization therapy: 12 of the 29 patients had a mean urine pH of ≥ 7.0, and 17 had a mean urine pH of < 7.0. The median OS from diagnosis was 95.6 months (95% confidence interval [CI] = 24.7-not reached), and from the start of alkalization therapy was 42.3 months (95% CI = 8.93-not reached). The median OS from the start of alkalization therapy in patients with a urine pH of ≥ 7.0 was not reached (n = 12, 95% CI = 3.0-not reached), which was significantly longer than that in patients with a pH of < 7.0 (15.4 months, n = 17, 95% CI = 5.8-not reached, p < 0.05).ConclusionsThe addition of alkalization therapy to standard therapies may be associated with more favorable outcomes in HCC patients with increased urine pH after alkalization therapy

DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation

In this work, we propose an AI-based method that intends to improve the conventional retinal disease treatment procedure and help ophthalmologists increase diagnosis efficiency and accuracy. The proposed method is composed of a deep neural networks-based (DNN-based) module, including a retinal disease identifier and clinical description generator, and a DNN visual explanation module. To train and validate the effectiveness of our DNN-based module, we propose a large-scale retinal disease image dataset. Also, as ground truth, we provide a retinal image dataset manually labeled by ophthalmologists to qualitatively show, the proposed AI-based method is effective. With our experimental results, we show that the proposed method is quantitatively and qualitatively effective. Our method is capable of creating meaningful retinal image descriptions and visual explanations that are clinically relevant.Comment: Accepted to IEEE WACV 202