PENERAPAN MEKANISME KEADILAN RESTORATIF (RESTORATIVE JUSTICE) DI TAHAP PENYIDIKAN OLEH KEPOLISIAN DAERAH SULAWESI UTARA

  Keadilan Restoratif atau sering dikenal dengan sebutan Restorative Justice merupakan prinsip baru penyelesaian tindak pidana dalam sistem peradilan pidana (criminal justice system) di Indonesia. Dalam hal ini penerapannya pada tahap penyidikan oleh Kepolisian Republik Indonesia secara umum dan secara khusus Kepolisian Daerah Sulawesi Utara. Mekanisme pendekatan keadilan restoratif (restorative justice) ini, lebih berorientasi pada rekonsiliasi antara pelaku (offender), korban (victim) dan masyarakat (community) untuk mengakomodir kepentingan masing-masing pihak. Meskipun prinsip ini masih baru dan kerap kali menjadi perdebatan oleh para ahli, namun penerapannya cukup sering digunakan sebagai sarana dalam memberikan rasa keadilan baik keadilan substantif maupun keadilan prosedur. Karena, dengan tingkat kejahatan yang tinggi dan secara simultan dengan overcapacity lembaga pemasyarakatan sehingga perlu mempertimbankan penerapan prinsip restorative justice dalam rangkaian criminal justice system di Indonesia. Berdasarkan data pendukung sejak tahun 2021 hingga bulan April 2023, bahwa Kepolisian Daerah Sulawesi Utara masih terbilang cukup rendah dalam mengedepankan restorative justice sehingga perlu dimasifkan penerapannya dan dibarengi dengan sosialisasi kepada seluruh stakeholders. Hal ini tentunya disebabkan oleh paradigma polisi maupun masyarakat terkait pemidanaan, masih berorientasi pada keadilan retributif (lex talionis). Tentunya dengan mengedepankan prinsip keadilan restoratif ini, agar dapat memberikan rasa keadilan kepada pihak-pihak terkait (pelaku, korban, keluarga, masyarakat dan negara). Kepolisian Daerah Sulawesi Utara memasifkan peningkatan sarana dan prasana dalam menunjang penereapan mekanisme keadilan restoratif melalui peresmian rumah restoratif justice atau disebut dengan Wale Bakubae.   Kata Kunci: Keadilan Restoratif, Penyidikan, Polisi, Kepolisian Daerah Sulawesi Utara, Wale Bakuba

Network interventions for managing the COVID-19 pandemic and sustaining economy.

Sustaining economic activities while curbing the number of new coronavirus disease 2019 (COVID-19) cases until effective vaccines or treatments become available is a major public health and policy challenge. In this paper, we use agent-based simulations of a network-based susceptible-exposed-infectious-recovered (SEIR) model to investigate two network intervention strategies for mitigating the spread of transmission while maintaining economic activities. In the simulations, we assume that people engage in group activities in multiple sectors (e.g., going to work, going to a local grocery store), where they interact with others in the same group and potentially become infected. In the first strategy, each group is divided into two subgroups (e.g., a group of customers can only go to the grocery store in the morning, while another separate group of customers can only go in the afternoon). In the second strategy, we balance the number of group members across different groups within the same sector (e.g., every grocery store has the same number of customers). The simulation results show that the dividing groups strategy substantially reduces transmission, and the joint implementation of the two strategies could effectively bring the spread of transmission under control (i.e., effective reproduction number ≈ 1.0)

Synaptic proximity enables NMDAR signalling to promote brain metastasis.

Metastasis-the disseminated growth of tumours in distant organs-underlies cancer mortality. Breast-to-brain metastasis (B2BM) is a common and disruptive form of cancer and is prevalent in the aggressive basal-like subtype, but is also found at varying frequencies in all cancer subtypes. Previous studies revealed parameters of breast cancer metastasis to the brain, but its preference for this site remains an enigma. Here we show that B2BM cells co-opt a neuronal signalling pathway that was recently implicated in invasive tumour growth, involving activation by glutamate ligands of N-methyl-D-aspartate receptors (NMDARs), which is key in model systems for metastatic colonization of the brain and is associated with poor prognosis. Whereas NMDAR activation is autocrine in some primary tumour types, human and mouse B2BM cells express receptors but secrete insufficient glutamate to induce signalling, which is instead achieved by the formation of pseudo-tripartite synapses between cancer cells and glutamatergic neurons, presenting a rationale for brain metastasis.This work was principally supported by grants from the Swiss National Science Foundation and the European Research Council, and by a gift from the Biltema Foundation that was administered by the ISREC Foundation, Lausanne, Switzerland

Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models

Abstract In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions